Employment opportunities, postdoctoral positions, summer jobs

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Postdoctoral position in natural language understanding, KU Leuven, Belgium

  • Employer: KU Leuven, Belgium
  • Title: Postdoctoral researcher
  • Specialty: Natural language understanding, machine learning
  • Location: Leuven, Belgium
  • Deadline: July 31, 2018
  • Date posted: June 18, 2018
  • Contact: sien.moens@cs.kuleuven.be

We offer a two-year postdoctoral position (extendable to four years) funded by the ERC (European Research Council) Advanced Grant CALCULUS “Commonsense and Anticipation enriched Learning of Continuous representations sUpporting Language UnderStanding” (http://calculus-project.eu). The principal investigator is Prof. Sien Moens. CALCULUS focuses on learning effective anticipatory representations of events and their narrative structures that are trained on language and visual data. The machine learning methods on which CALCULUS will build belong to the family of latent variable models where it will rely on Bayesian probabilistic models and neural networks as starting points. CALCULUS focuses on settings with limited training data that are manually annotated and especially aims at developing novel machine learning paradigms for natural language understanding. CALCULUS also evaluates the inference potential of the anticipatory representations in situations not seen in the training data and for inferring spatial, temporal and causal information in metric real world spaces. The best models for language understanding will be integrated in a demonstrator that translates language to events happening in a 3-D virtual world.

The successful candidate will have an opportunity to work on innovative natural language understanding research such as grounding language meaning into visual perception and translating narrative language into visual events. He or she will be given the opportunity for personal development beyond research, for example by contributing to teaching (e.g., at the undergraduate and graduate levels). For an outstanding candidate there is the potential to grow into an assistant professorship.


Responsibilities

  • Perform own research in language understanding and novel machine learning paradigms in the frame of the CALCULUS project.
  • Carry out some teaching duties, which may include lectures/exercise sessions, the organisation of student seminars, and the supervision of bachelor and master theses. 
  • Help in the supervision of PhD researchers of the CALCULUS team.

Prerequisites

  • You have (or are near completion of) a PhD in Computer Science (or a related field).
  • You have a motivated interest in fundamental research in language understanding and machine learning.
  • You are not afraid of creative and original ideas and solutions.
  • You have an outstanding track record of publications in relevant international peer-reviewed A ranked conferences and in relevant journals with high impact factor.
  • You are good at collaborating with and leading others.
  • You work proactively and independently and have good communication skills.
  • You have a very good knowledge of English, both spoken and written.
  • You are highly motivated, ambitious and result-oriented.

  Offer

  • We offer a 2 x 2-year postdoctoral position, starting in September 2018 (negotiable).
  • We offer a competitive wage and yearly budget to attend conferences and for short research stays.

  Interested If interested, send your CV including publication list, motivation letter and two recommendation letters by e-mail to Prof. dr. Sien Moens (sien.moens@cs.kuleuven.be) as soon as possible and the latest by July 31, 2018. For more information please contact Prof. dr. Sien Moens: tel.: +32 16 32 53 83. Excellent candidates will be invited for an interview (possibly via Skype).

The research team

The Language Intelligence & Information Retrieval (LIIR) lab (https://liir.cs.kuleuven.be) of KU Leuven, Belgium is part of the Human Computer Interaction (HCI) unit in the Department of Computer Science. The members of the lab are especially interested in natural language processing and understanding, multimedia mining, machine learning and information retrieval. They study well-informed theoretical models as well as challenging applications, often empowered by big data sets. They investigate probabilistic graphical and deep learning models, with a special focus on learning with limited supervision. LIIR has a special interest in statistical multimodal representation learning where we explore the complementarity of language and visual data. The developed technologies are, among others, applied in the domains of bioinformatics, business intelligence, e-commerce analytics, electronic message filtering, news search and mining, user generated content mining, and World Wide Web mining and search, and contribute to the field of data science in these areas. Through its collaborations LIIR connects to other disciplines including speech processing, computer vision, data mining, artificial intelligence, cognitive science, and human-computer interaction.

The university

KU Leuven is situated in a historic university town close to Brussels, the capital of Europe. It features an excellent Computer Science department providing a stimulating research environment. All programmes at this university are based on the innovative research of its scientists and professors. KU Leuven ranks among the best 50 universities worldwide!

Postdoctoral position in multilingual text mining, KU Leuven, Belgium

  • Employer: KU Leuven, Belgium
  • Title: Postdoctoral researcher
  • Specialty: Text mining, machine learning
  • Location: Leuven, Belgium
  • Deadline: July 31, 2018
  • Date posted: June 18, 2018
  • Contact: sien.moens@cs.kuleuven.be

We offer a two-year postdoctoral position funded by the EU ITEA3 project PAPUD "Profiling and Analysis Platform Using Deep Learning” (https://itea3.org/project/papud.html). The principal investigator is Prof. Sien Moens. The scope of the project is to build a universal model for data analytics using deep learning in order to help today’s businesses to make sense out of data. The postdoctoral position focuses on multilingual text mining and more specifically on interlingual content representations and methods of transfer learning with applications in multilingual topic modelling, content classification (e.g., opinion and argumentation mining) and question answering. The candidate will perform cutting-edge artificial intelligence research in the context of a European consortium composed of renowned academic and industrial partners.

  Responsibilities

  • Design and develop machine learning methods for multilingual text mining.
  • Carry out some teaching duties, which may include lectures/exercise sessions, the organization of student seminars, and the supervision of bachelor or master theses. 

  Prerequisites

  • You have (or are near completion of) a PhD in Computer Science (or a related field).
  • You have a motivated interest in and knowledge of text mining and machine learning, including probabilistic graphical models and deep learning. 
  • You have a solid track record of publications in relevant international peer-reviewed A ranked conferences and journals.
  • You have a profound interest in collaborating with the industry on applications of text mining and willing to contribute to a deep learning text analytics platform.
  • You have a very good knowledge of English, both spoken and written.
  • You are highly motivated, ambitious and result-oriented.

  Offer

  • We offer a two-year postdoctoral position, starting in September 2018 (negotiable).
  • We offer a competitive wage and yearly budget to attend conferences.

  Interested

If interested, send your CV including publication list, motivation letter and two recommendation letters by e-mail to Prof. dr. Sien Moens (sien.moens@cs.kuleuven.be) as soon as possible and the latest by July 31, 2018. For more information please contact Prof. dr. Sien Moens: tel.: +32 16 32 53 83. Excellent candidates will be invited for an interview (possibly via Skype).

The research team

The Language Intelligence & Information Retrieval (LIIR) lab (https://liir.cs.kuleuven.be) of KU Leuven, Belgium is part of the Human Computer Interaction (HCI) unit in the Department of Computer Science. The members of the lab are especially interested in natural language processing and understanding, multimedia mining, machine learning and information retrieval. They study well-informed theoretical models as well as challenging applications, often empowered by big data sets. They investigate probabilistic graphical and deep learning models, with a special focus on learning with limited supervision. LIIR has a special interest in statistical multimodal representation learning where we explore the complementarity of language and visual data. The developed technologies are, among others, applied in the domains of bioinformatics, business intelligence, e-commerce analytics, electronic message filtering, news search and mining, user generated content mining, and World Wide Web mining and search, and contribute to the field of data science in these areas. Through its collaborations LIIR connects to other disciplines including speech processing, computer vision, data mining, artificial intelligence, cognitive science, and human-computer interaction.

The university

KU Leuven is situated in a historic university town close to Brussels, the capital of Europe. It features an excellent Computer Science department providing a stimulating research environment. All programmes at this university are based on the innovative research of its scientists and professors. KU Leuven ranks among the best 50 universities worldwide!

PhD positions in DFG Graduate School AIPHES, TU Darmstadt

The Research Training Group “Adaptive Information Preparation from Heterogeneous Sources” (AIPHES), which has been established in 2015 at Technische Universität Darmstadt and at Ruprecht Karls University Heidelberg is filling two positions for three years, starting as soon as possible, located in Darmstadt and associated with UKP Lab (Prof. Iryna Gurevych). Positions remain open until filled. The positions provide the opportunity to obtain a doctoral degree with an emphasis in natural language processing tasks such as structured summaries of complex contents, abstractive summarization, or a related area. Applicants should be willing to work on cross-lingual, cross-modality and domain-independent methods. Prior experience in transfer learning, multi-task learning, adversarial learning, deep reinforcement learning or related methods is a plus.

The goal of AIPHES is to conduct innovative research in knowledge acquisition on the Web in a cross-disciplinary context. To that end, methods in computational linguistics, natural language processing, machine learning, computer vision, and data and information management will be developed. AIPHES investigates a novel, complex scenario for information preparation from heterogeneous sources. It interacts closely with end users who prepare textual documents in an online editorial office, and who should therefore benefit from the results of AIPHES. In-depth knowledge in one of the above areas is desirable but not a prerequisite.

AIPHES emphasizes close contact between the students and their advisors with regular joint meetings, a co-supervision by professors and younger scientists in the research groups, and an intensive exchange as part of the research and qualification program. Participating research groups at Technische Universität Darmstadt are Knowledge Engineering (Prof. Fürnkranz), Ubiquitous Knowledge Processing (Prof. Gurevych, Dr. Claudia Schulz), Machine Learning (Prof. Kersting), Visual Inference (Prof. Roth), Algorithmics (Prof. Weihe). Participants at Ruprecht Karls University Heidelberg are the Institute for Computational Linguistics (Prof. Frank) and the Natural Language Processing Group (Prof. Strube) of the Heidelberg Institute for Theoretical Studies (HITS). AIPHES strives to publish its results at leading scientific conferences and is actively supporting its doctoral researchers in this endeavor. The software that will be developed in the course of AIPHES should be put under the open source Apache Software License 2.0 if possible. Moreover, the research papers and datasets should be published with open access models.

Prerequisites

We are looking for exceptionally qualified candidates with a degree in Computer Science, Machine Learning, NLP, or a related study program. We expect the ability to work independently, personal commitment, team and communication abilities, as well as the willingness to cooperate in a multi-disciplinary team. Prior experience in scientific work is a plus. We specifically invite applications of women. Among those equally qualified, handicapped applicants will receive preferential consideration. International applications are particularly encouraged.

The research environment is excellent. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. UKP Lab is a highly dynamic research group committed to top-level conferences, technologies of the highest standards, cooperative work style and close interaction of team members. Its BMBF-funded Centre for the Digital Foundation of Research in the Humanities, Social, and Educational Sciences (CEDIFOR) emphasizes NLP, machine learning and text mining. The large-scale argument mining project allows searching large document collections in response to a user-defined topic: neural networks determine relevant pro and con arguments in real-time, and represent them in a concise summary.

Applications should include a motivational letter that refers to one of the planned research areas of AIPHES, a CV with information about the applicant’s scientific work, certifications of study and work experience, as well as a thesis or other publications in electronic form. Application materials must be submitted via the following form by June, 27th, 2018:

https://public.ukp.informatik.tu-darmstadt.de/aiphesrecruitment/

In addition, applicants should be prepared to solve a programming and a reviewing task in the first two weeks after their application.

Postdoc position: Bocconi University, Milan

  • Employer: Bocconi University
  • Title: Postdoctoral Researcher
  • Location: Milan, Italy
  • Deadline: June 22nd, 2018, 5 p.m.


  • Starting date: as early as possible, but no later than September 2018
  • Duration: 1 year


  • Date Posted: June 18, 2018
  • Contact: Paola Cillo (paola.cillo@unibocconi.it)
  • URL: https://bit.ly/2JW2tKZ (select the Gucci Lab call)

Gucci Research Lab (GRL) is a unique partnership between Bocconi University and Gucci to identify and study the trends that define the way in which organizations are evolving. This position is part of a larger project by the Gucci Lab at Bocconi on the effects of a change in a firm’s leadership positions on the firm’s culture and its performance. Part of the project involves the textual analysis of internal documents (e.g., emails), before and after the leadership change. To provide an example, textual analysis of these documents will be conducted to identify power relationships within the organization and study how they evolved over time.

REQUIREMENTS/QUALIFICATIONS



The successful candidate will work actively on novel directions in deep learning, multi-task learning, and neural networks.

The candidate is expected to have:

  • a Ph.D. or equivalent in Computer Science, Computational Linguistics/NLP,
Mathematics or related fields.
  • Good programming skills in Python.
  • Fluent English. Knowledge of other languages is more than welcome.
 Knowledge of Italian is NOT a requirement.
  • Knowledge of current neural network models, especially Word2Vec and Doc2Vec, and tools for neural networks (e.g. Tensorflow, Keras, PyTorch, etc.).
  • Publications in top-tier venues in the field of Computational Linguistics.
  • Experience in Ph.D. student supervision is a plus.
  • Salary: 43,310.50 euros per annum

HOW TO APPLY



The application must be sent to Faculty and Research Division of Bocconi University (addressing the Rector) just via email at recruiting_ricerca@unibocconi.it You can find more information about the project and call here: https://bit.ly/2t1DnAO

Postdocs: Johns Hopkins University

  • Employer: Johns Hopkins University
  • Title: Postdoctoral Researcher
  • Location: Baltimore, MD
  • Deadline: Applications will be accepted until positions are filled
  • Date Posted: June 6, 2018
  • Contact: clspsearch@clsp.jhu.edu
  • URL: https://www.clsp.jhu.edu/employment-opportunities/


The Center for Language and Speech Processing (CLSP) at the Johns Hopkins University seeks applicants for postdoctoral fellowship positions in speech, natural language processing and machine learning. Applicants must have a Ph.D. in a relevant discipline and a strong research record.

The center has a number of postdoctoral positions available. A single application will be considered for all open positions (except for one position as noted below). You need not indicate a specific position, but you may include a strong preference in an optional cover letter.

Example topics include:

  • Cross-lingual Information Retrieval
  • Trend Detection in Social Media
  • Social Media and Mental Health
  • Analysis of Clinical Medical Text
  • Broadly Multilingual Learning of Morphology and Low-Resource Machine Translation
  • NLP and Machine Learning for Clinical Data Analysis

Johns Hopkins University is a private university located in Baltimore, Maryland. The campus provides easy access to a number of affordable and vibrant neighborhoods and waterfront dining options. Hopkins is also connected to Washington DC (40 mins), Philadelphia (1.5 hours) and New York city (2.5 hours) via direct trains and buses.

CLSP is one of the world’s largest academic centers focused on speech and language. CLSP is home to a dozen faculty members, half a dozen postdocs, and over 60 graduate students. It has a history of placing students in top academic and industry positions, with a large network of alumni at Google, Amazon, Microsoft Research, Bloomberg, IBM Research, Facebook, Twitter, Nuance, BBN, and numerous startups.

Applicants are not required to be to US citizens or permanent residents.

Questions about specific projects should be directed to the contact information associated with the project. General inquiries may be sent to clspsearch@clsp.jhu.edu.

Details and application information: http://www.clsp.jhu.edu/employment-opportunities/


Research Fellow in Software Engineering with a Focus on Natural Language Processing at University of Tartu, Estonia

  • Employer: University of Tartu, Institute of Computer Science, Software Engineering group
  • Title: Research Fellow
  • Speciality: Software engineering, machine learning, natural language processing
  • Location: Tartu, Estonia
  • Deadline: June 4, 2018
  • Date posted: May 21, 2018
  • Contact: Dietmar Pfahl, Kairit Sirts (<firstname>.<lastname>@ut.ee)

Postdoctoral position
Applications are invited for a position of Research Fellow at the Software Engineering and Information Systems Research Group, Institute of Computer Science, University of Tartu. The institute is the leading Computer Science department in the Baltics and is one of the top-2 in Central and Eastern European universities according to the field-specific Times Higher Education Ranking 2017. The Software Engineering and Information Systems group conducts research in the fields of data-driven software engineering decision support, business process management, and secure information systems design. The group is composed of 25 members, including 12 PhD students. The group places a strong emphasis on research excellence and quality of its research publications. The institute has strong ties with the local industry and manages a portfolio of half a dozen research projects in cooperation with industry partners.

The successful candidate will conduct research in the field of data-driven software engineering decision support, within a team that brings together researchers specialized in software analytics, software evolution, software quality assurance, agile development methods, data mining and natural language processing. The research fellow will be expected to contribute to ongoing research projects which aim at exploiting advanced data science methods in one or more of the following application domains:

  • open innovation,
  • energy-efficient software development,
  • software testing.

The research to be conducted is interdisciplinary. In particular, we will be closely collaborating with the natural-language processing group to leverage their expertise on analyzing unstructured data.

Requirements
Candidates must have a PhD in Computer Science or a related discipline. Expertise in at least one of the following topics is essential: software testing, static code analysis, software evolution/maintenance, machine learning. Experience in developing research prototypes and working in collaborative research projects is desirable. The position is not term-limited. Funding is already secured for the first two years of the appointment. The continuation of the position after the first two years will depend on further funding. Remuneration will be up to 2400 euros/month. Estonia applies a flat income tax of 20% on salaries and provides public health insurance for employees.

The expected start date is 1 September 2018, but a later start date can be negotiated.

The deadline for applications is 4 June 2018. The application procedure is outlined in the official advertisement at the University's website.

Postdoctoral research positions in cybersecurity, natural language processing, and experimental social psychology at SUNY Albany

  • Employer: University at Albany, Research Foundation of the State University of New York, ILS Institute
  • Title: Postdoctoral researcher
  • Speciality: Cybersecurity, natural language processing, machine learning, experimental design
  • Location: Albany, New York, USA
  • Deadline: July 31, 2018
  • Date posted: May 18, 2018
  • Contact: Tomek Strzalkowski (tomek {at} albany.edu)

Postdoctoral positions

  • The Personalized AutoNomous Agents Countering Social Engineering Attacks (PANACEA) Project. The PANACEA Project is a joint effort of communication and computer science faculty at the University at Albany, SUNY, as well as researchers at other institutions. The project aims to design, develop, and evaluate an automated system that will protect online users against current and future forms of social engineering attacks. The system will serve as an intermediary between attackers (human, automated, hybrid, coordinated) and the potential victims they target by addressing and eliminating human vulnerabilities in current cyber defense capabilities. The objectives of the project include detection and classification of social engineering attacks as well as active defenses, including engaging and identifying of the attackers.
  • The Computational Ethnography from Metaphors and Polarized Language (COMETH) Project. The COMETH project is a joint effort of computer science and psychology faculty at the University at Albany. The project aims to develop and validate novel computational methodology for automatically acquiring cultural models that represent the worldviews of communities and subcultures operating within the larger society. These models will be obtained using advanced natural language processing and machine learning techniques on data from online media outlets produced by different communities. The objectives of this research include (a) capturing prevalent community attitudes (sentiment and beliefs) toward key concepts such as government, rights, economic inequality, etc.; (b) showing how these attitudes evolve over time, including in response to external influences (e.g., national or international events); and (c) explaining how this system of attitudes acts like an interpretive and defensive tool by allowing the community to reject or distort incoming information.

Requirements for the PANACEA position
For the PANACEA project, we seek a postdoctoral researcher to join our interdisciplinary team. The candidate must have a Ph.D. in Computer Science from an accredited college or university that was granted within the last 3 years, and evidence of advanced scholarly research in a relevant field. This position starts September 1, 2018.

  • The candidates are expected to have the following skills: in-depth knowledge of current issues and methods in cybersecurity, natural language processing, socio-behavioral computing, human-computer dialogue, statistical methods of data analysis, and machine learning. Familiarity with empirical method of data collection and model validation is preferred. Programming expertise is essential including substantial experience with Java, Python, and C++ programming environment, as well as with Windows, Linux and Unix platforms. Linguistic background and familiarity with methods of conversational analysis is a plus.

Requirements for the COMETH positions
For the COMETH project, we seek two postdoctoral researchers: one in computer science and one in psychology. The candidates must have a Ph.D. in Computer Science or Psychology from an accredited college or university that was granted within the last 3 years, and evidence of advanced scholarly research in a relevant field. These positions start December 1, 2018.

  • The computer science candidates are expected to have the following skills: in-depth knowledge of current issues and methods in natural language processing, data science, domain modeling, socio-behavioral computing, statistical methods, and machine learning. Familiarity with empirical method of data collection and model validation is preferred. Programming expertise is essential including substantial experience with Java, Python, and C++ programming environment, as well as with Windows, Linux and Unix platforms. Linguistic background and familiarity with sentiment analysis and metaphor extraction is a plus.
  • The psychology candidates are expected to have following skills: substantial experience with experimental design and advanced statistical methods in experimental social psychology, and knowledge of political psychology. Experience with open science and pre-registration of research protocols will be beneficial.

Overall Requirements

  • For all postdoctoral researchers: duties include advanced research and development under the direction of the project faculty, report preparation and coordination of work of graduate student assistants. Ability to execute substantial tasks within large projects in timely fashion is essential. Candidates must also address in their applications, their ability to work with a culturally diverse population.

The postdoctoral researcher appointment review will begin immediately and will close once filled. The successful candidates will be located in the Institute for Informatics, Logics, and Security Studies at the University at Albany, SUNY. The appointment is for 40 hours a week, initially for 12 to 18 months, and potentially extendible for up to 48 months, depending on the project. Expected start dates are September 1, 2018 and December 1, 2018, pending funding approval from the Federal Government sponsor. The salary is commensurate with experience.

How to Apply

Interested individuals should direct inquiries and submit a cover letter, resume, and three letters of reference to: Prof. Tomek Strzalkowski, Director ILS Institute, University at Albany, tomek {at} albany.edu

Two PhD positions in deep learning for natural language understanding and summarisation at Idiap, Switzerland

  • Employer: Idiap Research Institute, Natural Language Understanding group
  • Title: Two PhD positions
  • Speciality: Natural Language Understanding, Summarisation, Machine Learning
  • Location: Martigny, Switzerland
  • Deadline: May 31, 2018
  • Date posted: April 30, 2018
  • Contact: James Henderson (james.henderson@idiap.ch)

Two PhD positions

The Idiap Research Institute seeks qualified candidates for two PhD student position in the field of natural language understanding, developing deep learning methods for textual entailment and opinion summarisation.

The research will be conducted in the framework of the Swiss NSF funded project Learning Representations of Abstraction for Opinion Summarisation. One of the successful candidates will investigate modelling abstraction relationships between texts (textual entailment), and the other will investigate summarising large collections of opinions (opinion summarisation). Opinion summarisation must abstract away from the details of individual opinions to find consensus statements which are entailed by a significant proportion of opinions.

This project will model these natural language understanding tasks through fundamental advances in representation learning and deep learning architectures. The work will start from Dr. Henderson's work on modelling abstraction in deep learning architectures, where learned vectors represent entailment rather than the usual similarity. Successes in the unsupervised learning of word vectors for entailment will be extended to deep learning architectures for the compositional semantics of texts. Methods for finding the intersection of information in vectors will be extended to clustering texts by their shared content and generating abstract summaries.

The ideal PhD candidate should hold a Master degree in computer science, computational linguistics or related fields. She or he should have a background in machine learning, optimisation, or natural language processing. The applicant should also have strong programming skills.

The successful PhD candidates will join the Natural Language Understanding group at Idiap, under the supervision of Dr. James Henderson. They will also become doctoral students at EPFL conditional on parallel application to, and acceptance by, the EPFL Doctoral School. Appointment for the PhD position is for a maximum of 4 years, provided successful progress, and should lead to a dissertation. Annual gross salary ranges from 47,000 Swiss Francs (first year) to 50,000 Swiss Francs (last year). Starting date is to be negotiated, within 2018. All queries related to the advertised position can be sent to Dr. James Henderson (james.henderson@idiap.ch).

Please apply online here: http://www.idiap.ch/webapps/jobs/ors/applicant/position/index.php?PHP_APE_DR_9e581720b5ef40dc7af21c41bac4f4eb=%7B__TO%3D%27detail%27%3B__PK%3D%2710241%27%7D

Idiap

Idiap is an independent, not-for-profit, research institute funded by the Swiss Federal Government, the State of Valais, and the City of Martigny. It is located in a scenic region in the south of Switzerland, surrounded by the highest mountains of Europe, and offering exceptional quality of life, exciting recreational activities, including hiking, climbing and skiing, as well as varied cultural activities. It is within close proximity to Lausanne and Geneva. Idiap is an equal opportunity employer and is actively involved in the "Advancement of Women in Science" European initiative.


2 postdoctoral research positions in text mining and natural language understanding at KU Leuven, Belgium

  • Employer: KU Leuven, Department of Computer Science, Language Intelligence and Information Retrieval lab
  • Title: Postdoctoral researcher
  • Speciality: Text mining, natural language understanding, machine learning
  • Location: Leuven, Belgium
  • Deadline: May 21, 2018
  • Date posted: April 23, 2018
  • Contact: Sien Moens (sien.moens {at} cs.kuleuven.be)

Postdoctoral positions

  • Postdoctoral position on the topic of multilingual text mining. The goal is to build interlingual representations that allow multilingual topic modelling, content classification (e.g., opinion and argumentation mining) and question answering. This postdoctoral position will be funded by the EU ITEA3 grant PAPUD and offers a contract for two years. The position will start as soon as possible.
  • Postdoctoral position on the topic of multimodal representation learning. The goal is to learn continuous representations that represent language grounded in visual perception (static images and video), assist in the design of novel machine learning architectures, and investigate suitable data structures for real-time search of the representations. This postdoctoral position will be funded by the ERC (European Research Council) Advanced Grant CALCULUS and offers a contract for two years (with the possibility of renewal for another two years). The position will start September 1, 2018.

Requirements

  • PhD in computer science or equivalent.
  • Motivated interest in and preferably knowledge of (as demonstrated by publications in highly recognized venues such as ACL, EMNLP, ICML, NIPS, etc.) of natural language processing and machine learning, including deep learning and learning of latent variable models. For the second postdoctoral position, interest or experience in semantic hashing is a plus.

The successful candidates will have an opportunity to work on cutting-edge natural language processing, multimedia processing and machine learning research. He/she will be given the opportunity for personal development beyond research, for example by contributing to departmental teaching at the undergraduate and graduate levels. KU Leuven is an institution for research and education with international appeal. All programmes at this University are based on the innovative research of its scientists and professors. KU Leuven ranks among the best 50 universities worldwide!

2 PhD positions in natural language understanding at KU Leuven, Belgium

  • Employer: KU Leuven, Department of Computer Science, Language Intelligence and Information Retrieval lab
  • Title: PhD researcher
  • Speciality: Natural language understanding, machine learning
  • Location: Leuven, Belgium
  • Deadline: May 21, 2018
  • Date posted: April 23, 2018
  • Contact: Sien Moens (sien.moens {at} cs.kuleuven.be)

PhD positions

  • PhD position on the topic of multimodal representation learning trained on language and visual data. The goal is to learn continuous representations of language grounded in visual data (static images and video) including the design, implementation and evaluation of novel machine learning architectures that capture textual as well as visual grammars. The learned representations will serve as commonsense knowledge in language understanding tasks. This PhD position will be funded by the ERC (European Research Council) Advanced Grant CALCULUS. The position starts September 1, 2018.
  • PhD position on the topic of semantic parsing of natural language sentences and discourse. The goal is to learn compositional models that take into account continuous representations of objects, their attributes and likely relationships. An additional focus is on using the compositional models to efficiently parse language in real-time. This PhD position will be funded by the ERC (European Research Council) Advanced Grant CALCULUS. The position starts September 1, 2018.

Requirements

  • Master degree in computer science or equivalent.
  • Motivated interest in and preferably knowledge of (as demonstrated in master thesis or master course work) of natural language processing, machine learning, including deep learning and learning of latent variable models, semi-supervised machine learning, and constrained optimization.

The successful candidates will have an opportunity to work on cutting-edge natural language processing, multimedia processing and machine learning research. He/she will be given the opportunity for personal development beyond research, for example by contributing to departmental teaching at the undergraduate and graduate levels. KU Leuven is an institution for research and education with international appeal. All programmes at this University are based on the innovative research of its scientists and professors. KU Leuven ranks among the best 50 universities worldwide!

Associate Research Scientist (NLP, machine learning and text mining), TU Darmstadt

The Ubiquitous Knowledge Processing (UKP) Lab at the Department of Computer Science, Technische Universität (TU) Darmstadt, Germany has an opening for an

Associate Research Scientist (PostDoc- or PhD-level; for a term of three years with an extension option)

This position is intended to strengthen the profile of the lab in a research area within natural language processing (NLP), machine learning and text mining, such as word-/sentence-/discourse-level semantics, robust textual inference, and the applications of the above in higher-level NLP, such as QA, text summarization, argument mining, etc. The lab closely cooperates with the groups in machine learning, computer vision, and interactive data analytics of the Computer Science department and many other research labs and companies. Besides, the lab conducts research projects in close cooperation with the users in the humanities and social sciences.

We ask for applications from highly qualified candidates with a specialization/PhD in NLP/Text Mining, preferably with relevant research and teaching experience and strong communication skills in English and German (optional). Individual career development plans can be worked out. E.g. the successful candidate will contribute to research activities described above and – based on the previous experience and qualifications – will be given an opportunity to grow, i.e. to teach courses, co-supervise PhD students, and manage research projects. Outstanding candidates (at M.Sc.-level, without a PhD) are invited to apply and can be considered for a PhD-level position with an adjusted scope of responsibilities. The position being filled is based on the university funds.

The research environment is excellent. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones among the German universities. Its unique Research Training Group “Adaptive Information Processing of Heterogeneous Content” (AIPHES) funded by the DFG and the BMBF-funded Centre for the Digital Foundation of Research in the Humanities, Social, and Educational Sciences (CEDIFOR) emphasize NLP, machine learning and text mining. UKP Lab is a highly dynamic research group committed to top-level conferences, technologies of the highest standards, cooperative work style and close interaction of team members.

Applications should include a detailed CV, a motivation letter, an outline of previous working or research experience and the names of three referees. Applications from women are particularly encouraged. All other things being equal, candidates with disabilities will be given preference. Please submit your application via the application form by March 28, 2018. The position is open until filled.

PhD positions in DFG Graduate School AIPHES, TU Darmstadt: Natural Language Processing and Machine Learning

The Research Training Group "Adaptive Information Preparation from Heterogeneous Sources" (AIPHES), which has been established in 2015 at the Technische Universität Darmstadt and at the Ruprecht‑Karls‑University Heidelberg is filling two positions for three years, starting as soon as possible, located in Darmstadt and associated with UKP Lab (Prof. Iryna Gurevych). Positions remain open until filled.

The positions provide the opportunity to obtain a doctoral degree with an emphasis in natural language processing tasks such as structured summaries of complex contents, in content selection and classification enhanced by reasoning, or a related area.

The goal of AIPHES is to conduct innovative research in knowledge acquisition on the Web in a cross-disciplinary context. To that end, methods in computational linguistics, natural language processing, machine learning, network analysis, computer vision, and automated quality assessment will be developed. AIPHES will investigate a novel, complex scenario for information preparation from heterogeneous sources. It interacts closely with end users who prepare textual documents in an online editorial office, and who should therefore profit from the results of AIPHES. In-depth knowledge in one of the above areas is desirable but not a prerequisite.

AIPHES emphasizes close contact between the students and their advisors with regular joint meetings, a co-supervision by professors and younger scientists in the research groups, and an intensive exchange as part of the research and qualification program. Participating research groups at Technische Universität Darmstadt are Knowledge Engineering (Prof. Fürnkranz), Ubiquitous Knowledge Processing (Prof. Gurevych, Dr. Claudia Schulz), Machine Learning (Prof. Kersting), Visual Inference (Prof. Roth), Algorithmics (Prof. Weihe). Participants at Ruprecht Karls University Heidelberg are the Institute for Computational Linguistics (Prof. Frank) and the Natural Language Processing Group (Prof. Strube) of the Heidelberg Institute for Theoretical Studies (HITS). AIPHES strives to publish its results at leading scientific conferences and is actively supporting its doctoral researchers in this endeavor. The software that will be developed in the course of AIPHES should be put under the open source Apache Software License 2.0 if possible. Moreover, the research papers and datasets should be published with open access models.

Prerequisites

We are looking for exceptionally qualified candidates with a degree in Computer Science, Machine Learning, NLP, or a related study program. We expect ability to work independently, personal commitment, team and communication abilities, as well as the willingness to cooperate in a multi-disciplinary team. Prior experience in scientific work is a plus. Applicants should be willing to work on lesser-resources languages, e.g. German, and, if necessary, to acquire basic German language skills during the training program. We specifically invite applications of women. Among those equally qualified, handicapped applicants will receive preferential consideration. International applications are particularly encouraged.

The research environment is excellent. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. UKP Lab is a highly dynamic research group committed to top-level conferences, technologies of the highest standards, cooperative work style and close interaction of team members. Its BMBF-funded Centre for the Digital Foundation of Research in the Humanities, Social, and Educational Sciences (CEDIFOR) emphasizes NLP, machine learning and text mining. The large-scale argument mining project allows searching large document collections in response to a user-defined topic: neural networks determine relevant pro and con arguments in real-time, and represent them in a concise summary.

Applications should include a motivational letter that refers to one of the planned research areas of AIPHES, a CV with information about the applicant’s scientific work, certifications of study and work experience, as well as a thesis or other publications in electronic form. Application materials must be submitted via the following form by April 3rd, 2018: https://public.ukp.informatik.tu-darmstadt.de/aiphesrecruitment/

In addition, applicants should be prepared to solve a programming and a reviewing task in the first two weeks after their application.

Tenure track at IDSIA (Switzerland) in Natural Language Understanding and Text Mining

  • Employer: IDSIA (www.idsia.ch)
  • Title: Tenure track
  • Specialty: Natural Language Understanding and Text Mining
  • Location: Lugano, Switzerland
  • Deadline: March 31th, 2018 (start date flexible)
  • Date posted: March 16, 2018
  • Contact email: Giorgio Corani (giorgio.corani@supsi.ch)

Project Description
The person hired on this position will evenly share her/his working time on two main activities:

  • Basic research, aiming at publications in highly rated journals and international conferences;
  • Applied research, collaborating with industrial partners in cutting-edge projects.

A further cross-activity will be contributing to search for funding opportunities of both basic and applied research.

The involved research area regards natural language understanding (probabilistic language modeling, keyword extraction, text summarization), text mining (text classification, word embedding, topic models) and semantic analysis of the text.

Requirements

  • The position is for a young researcher who has already obtained a PhD on a topic relevant to Natural Language Processing and/or Text Mining;
  • Master in informatics or other areas with strong emphasis on computation;
  • Excellent programming skills and deep knowledge of libraries for natural language processing;
  • Communication and collaboration skills.
  • Proficiency in written and spoken in English.


Optional but preferential

  • Strong publications record;
  • Capability of leading projects carried out with industrial partners on automatic analysis of documents;
  • Good knowledge of machine learning algorithms and tools;
  • Good knowledge of written and spoken Italian, or willingness to learn it in short times.

We offer

  • A tenure track position (degree of occupancy 100%)
  • International working environment;
  • Collaboration with a strong team of researchers in Machine Learning and Statistics (our publication record is available at: [1]);
  • Salary starting from 80,000 CHF / year (about 84,000 $/year)

Application
Applicants should submit the following documents, written in English:

  • curriculum vitae
  • list of exams and grades obtained during the Bachelor and the Master of Science;
  • list of three references (with e-mail addresses);
  • brief statement on how their research interests fit the topics above (1-2 pages);
  • publications list and possibly link to the thesis.

Applications should be submitted through the Application website

Postdoctoral position in Psychology at University of Pennsylvania

  • Employer: University of Pennsylvania
  • Title: Postdoctoral Researcher
  • Specialty: Computational Linguistics
  • Location: Philadelphia, Pennsylvania
  • Deadline: March 20th, 2018 (start date flexible)
  • Date posted: February 27, 2018
  • Contact email: Sudeep Bhatia (bhatiasu@sas.upenn.edu)

Project Description
The researcher will study how word embeddings and related techniques can be used to model how people represent objects and events, and, in turn, predict human probability judgment, event forecasting, social judgment, risk perception, and consumer behavior.

Requirements
Candidates should have completed (or should be about to complete) a PhD in Computer Science, Psychology , Cognitive Science, or related fields, and should be interested in applying natural language processing to address questions in Psychology, Policy, Economics, and Marketing. Applicants should have graduate-level expertise in natural language processing and machine learning.

Additional Details
The position will be based in the Psychology department at the University of Pennsylvania, and will be supervised by Sudeep Bhatia. Lyle Ungar will serve as a second advisor. The postdoctoral researcher will also be encouraged to interact with the broader intellectual community at Penn. The appointment is expected to begin Sept 1, 2018 (start date is flexible). The term of hiring is one year, renewable for up to one additional year. The position is full time and there are no teaching or administrative responsibilities.

How to Apply
Applications for the positions must be submitted by March 20th, 2018, to ensure full consideration. However, review of applications will continue until the position is filled. Applicants should email a curriculum vitae and contact information for two references to Sudeep Bhatia at bhatiasu@sas.upenn.edu.

Postdoctoral Research Scientist: Computational Linguistics - Rochester Institute of Technology

  • Employer: Rochester Institute of Technology
  • Title: Postdoctoral Research Scientist
  • Specialty: Postdoctoral Research Scientist: Computational Linguistics
  • Location: Rochester, New York, United States
  • Deadline: Open until filled
  • Date posted: February 17, 2018
  • Contact: Cecilia O. Alm (coagla@rit.edu)
  • Job listing

We invite applications for an interdisciplinary postdoctoral position with specialization in computational linguistics and/or technical or scientific methods in language science at Rochester Institute of Technology (RIT), in Rochester, NY. This is a one-year position with opportunity for renewal. The applicant should demonstrate a fit with our commitment to collaborate with colleagues across the university on research initiatives in Personalized Healthcare Technology. In addition to engaging in research projects, the right candidate will be able to teach a total of two courses per year - one course each in the College of Liberal Arts and the Golisano College of Computing and Information Sciences at RIT. The teaching assignment may be Computer Science Principles, Introduction to Language Science, Language Technology, Introduction to Natural Language Processing, Science and Analytics of Speech (acoustic and experimental phonetics), Spoken Language Processing (automatic speech recognition and text-to-speech synthesis), Seminar in Computational Linguistics, or another course depending on background.

Required Minimum Qualifications:

  • PhD., with training in Computational Linguistics, Linguistics, or an allied field
  • Advanced graduate coursework in computational linguistics (natural language processing or speech processing), linguistics, or language science broadly
  • Publication record and plan for research and grant seeking activities
  • Ability to contribute in meaningful ways to our commitment to cultural diversity, pluralism, and individual differences

Required Application Documents:
Cover Letter, Curriculum Vitae or Resume, List of References, Research Statement

How To Apply:
Please apply at: http://careers.rit.edu/staff. Click the link for search openings and in the keyword search field, enter the title of the position or 3599BR.

Postdoctoral Research Fellow: Automated Text Analysis - University of Michigan

  • Employer: University of Michigan
  • Title: Research Fellow
  • Specialty: Postdoctoral Research Fellow: Automated Text Analysis
  • Location: Ann Arbor, Michigan, United States
  • Deadline: March 12, 2018, desired start June 2018
  • Date posted: February 12, 2018
  • Official Job Listing - Application Page

How to Apply
A cover letter is required for consideration for this position and should be included as the first page of your CV. The cover letter should outline your specific interest in the position and outline skills and experience that directly relates to this position.

Job Summary
A generously funded project (2016-2021) welcomes applicants with interest in and expertise in automated text analysis. The project, M-Write, brings together researchers from writing studies, the natural sciences, and computer programming to investigate how Writing-to-Learn pedagogies promote conceptual learning by students in large-enrollment gateway courses. Students write in response to carefully crafted assignments, participate in automated peer review, and then revise their drafts. Approximately 10,000 students have already participated in M-Write courses, which means that there is already a large corpus of student writing and peer review responses, with more being added each semester. We seek a specialist in automated text analysis to join the research team. Goals include principled corpus compilation, analysis, and development of corpus-driven algorithms to support student learning of key concepts highlighted in assignments.

This position will be a 12-month Post-Doctoral Fellowship beginning in June 2018 with possibility of renewal. The salary is negotiable.

Responsibilities

  • Retrieve and create corpora for NLP and associated linguistic analysis
  • Develop and test systems for identifying students who appear to lack key concepts as defined by lexical and grammatical structures, discourse analysis, and possible sentiment analysis
  • Design a data warehouse that will facilitate the ability to collect, manage, and archive data including student interviews, student surveys, student writing samples, and academic and demographic data for students in order to conduct research leading to peer-reviewed publications, and future external funding
  • In collaboration with other project staff collect, manage, and archive data including student interviews, student surveys, student writing samples, and academic and demographic data for students in order to conduct research that could lead to peer-reviewed publications
  • Organize, synthesize, and analyze project data, and write co-authored papers with project PIs for peer-reviewed articles

Required Qualifications
Ph.D. in computational linguistics, natural language processing, machine learning, cognitive linguistics, sentiment analysis or related area. Previous research experience in textual analysis in terms of syntax (e.g. parts of speech tagging), semantics (e.g. topic segmentation) and discourse analysis is essential. Research in statistical modeling is also required. Strong computer science skills are essential, and data warehouse design skills are highly desired. Background in the theory and research of writing-to-learn pedagogies and experience with educational technology in natural language processing are desired but not required.

Background Screening
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks will be performed in compliance with the Fair Credit Reporting Act.

U-M EEO/AA Statement
The University of Michigan is an equal opportunity/affirmative action employer.


CU Boulder: Postdoc in Machine Learning with an Emphasis on Speech and Language Processing

  • Employer: University of Colorado Boulder
  • Title: Postdoctoral Associate
  • Specialty: Machine Learning, Speech and Language Processing
  • Location: Boulder, Colorado, United States
  • Deadline: Ongoing, desired start August 2018
  • Date posted: February 9, 2018
  • Contact: Dr. Sidney D’Mello

Postdoc in Machine Learning with an Emphasis on Speech and Language Processing
(Department of Computer Science and Institute of Cognitive Science at the University of Colorado Boulder)

The Department of Computer Science at the University of Colorado Boulder anticipates hiring a full time postdoctoral research associate starting August 2018 for one year and renewable for a second (and third) year. The position includes a competitive salary commensurate with experience and full benefits. Review of applications will begin immediately and continue until the position is filled.

The successful candidate will conduct research in machine learning applied to speech and language processing to solve challenging, but impactful, real-world problems. He/she will participate in the development and application of advanced machine learning techniques (e.g., deep recurrent neural networks) to multi-party speech data collected in authentic contexts (e.g., classroom discourse, small group collaborative problem solving).

The candidate will work under the supervision of Dr. Sidney D’Mello and will play a collaborative and co-leadership role in a vibrant research team encompassing researchers in Computer Science and the Institute of Cognitive Science.

The position offers a unique postdoctoral training experience and unsurpassed publishing opportunities within multi-department and multi-institution grant-funded projects. The postdoc will be encouraged to develop new technical skills, strengthen their research portfolios via peer-reviewed publications, gain interdisciplinary experience by working with a diverse team, develop leadership skills by mentoring students, and gain expertise in co-authoring grant proposals.

Required

  • Ph.D. in Computer Science, Artificial Intelligence, or a related field at the time of hire
  • Research experience in advanced machine learning (e.g., deep learning, probabilistic graphical models)
  • Evidence of a strong publication record in the aforementioned areas

Desired

  • Research experience in one or more of the following areas: acoustic signal processing, automatic speech recognition, natural language understanding, discourse modeling

Job Details

  • One year initial position with possible extension to a second and third year based on performance and availability of funds
  • Desired start date is August 2018. However, start date is negotiable
  • Competitive salary with benefits commensurate with qualifications

How to Apply
Please complete the following form: https://tinyurl.com/CUPostDoc1 and upload the following required documents: (1) cover letter, (2) current CV, and (3) one or two representative publications as a single PDF document named FirstNameLastName.pdf.

Additional documents will be required from candidates selected for further review after the initial documents are received: (4) – The document uploaded for Proof of Degree can be a Transcript which shows the date the degree was conferred; Copy of Diploma; or official letter from the Registrar or the Dean of the School or College conferring the degree; and (5) – List of references

About the University of Colorado and the City of Boulder
The University of Colorado Boulder is a widely recognized and respected research university in the U.S. Its 11 research institutes house more than 900 researchers, students, and staff, making a major contribution to the research infrastructure of the university and local economy. Boulder is one of the 34 public research institutions belonging to the Association of American Universities (AAU). It contributes to the local community via research partnerships, education opportunities, and development projects.

The city of Boulder hosts thriving tech industries, supports a renowned entrepreneurial community, has some of the region's best restaurants, and is home to many federal research labs. There are endless ways to enjoy Boulder's 300-plus days of sunshine a year — from the hundreds of miles of hiking and biking trails to some of the country's finest microbrews. It's also quick and easy to get around by bus or bike and a quick 25 mile ride to nearby Denver.

Special Instructions to Applicants
The University of Colorado Boulder conducts background checks on all final applicants being considered for employment, prior to the issuance of an offer letter. The University of Colorado Boulder is committed to providing a safe and productive learning, living and working community. To achieve this goal, we conduct background investigations for all final applicants being considered for employment. Background investigations include a criminal history record check, and an EPLS (Excluded Parties List System) check. The Immigration Reform and Control Act requires that verification of employment eligibility be documented for all new employees by the end of the third day of work.

The University of Colorado is an equal opportunity and affirmative action employer committed to assembling a diverse, broadly trained faculty and staff. In compliance with applicable laws and in furtherance of its commitment to fostering an environment that welcomes and embraces diversity, the University of Colorado does not discriminate on the basis of race, color, creed, religion, national origin, sex (including pregnancy), disability, age, veteran status, sexual orientation, gender identity or expression, genetic information, political affiliation or political philosophy in its programs or activities, including employment, admissions, and educational programs. Inquiries may be directed to the Boulder Campus Title IX Coordinator by calling 303-492-2127. In accordance with the Americans with Disabilities Act, alternative formats of this ad can be provided upon request for individuals with disabilities by contacting Human Resources at adacoordinator@colorado.edu.


Full-time Researchers, IBM Research - Almaden

  • Employer: IBM Research - Almaden
  • Title: Research Staff Member
  • Speciality: Natural Language Processing, Computational Linguistics, Machine Learning, or related areas
  • Location: San Jose, California, USA
  • Deadline: June 1, 2018
  • Date posted: January 31, 2018
  • Contact: Yunyao Li (yunyaoli {at} us.ibm.com) and/or Lucian Popa (lpopa {at} us.ibm.com)


IBM Research - Almaden is looking for talented researchers with background in Natural Language Processing and Machine Learning to join the Natural Language Processing, Entity Resolution and Discovery Department. We are actively building the next-generation knowledge engineering platform for the creation, maintenance and consumption of "industry-specific" knowledge bases from a large number of (un/semi)structured public, licensed and enterprise content sources. Such industry-specific knowledge bases are the foundation of many emerging AI services and applications.

Such a platform needs to support the entire life cycle for knowledge engineering including:

  • Creation, maintenance and evolution of domain schema to capture domain concepts of interest
  • Scalable systems, tools and methodologies to support the development of individual analytic stages involved in creating knowledge from multiple sources, including text analytics, entity resolution and integration, cleansing, data transformations and machine learning
  • Domain adaptability and easy-to-use interfaces for key user personae for the individual analytic stages
  • Scalable content services infrastructure that enables production-level deployment of these analytic workflows with support for continuous monitoring, introspection and recovery in the knowledge base creation process
  • Techniques and methods for scalable and flexible indexing and querying support over the knowledge base supporting structured and search style queries, entity queries, as well as ad-hoc and exploratory queries
  • Easy-to-use knowledge consumption interfaces for both human and machine consumption including support for discovery and ad-hoc NLQ driven interfaces
  • Incorporating crowd sourcing and continuous evolution of the system through learning from user interactions

The research builds upon and extends successful projects from our group, which have resulted in significant academic, industrial and open source impact.

The research is being conducted in close collaboration with the IBM Watson division and multiple global IBM Research labs (Australia, India, and Zurich), with focus around demonstrating scalable knowledge base construction in multiple industry domains (e.g., Healthcare, Financial and consumer domains).

We are currently looking for talented researchers with interest in system design and implementation as well as experience in one or more areas relevant to the knowledge engineering life cycle as described above, particularly those with background in Natural Language Processing and Machine Learning. You will participate in cutting edge research, build at industrial scale, and keep connected to the larger research community by regularly publishing papers and partnering with universities.

Required

  • Bachelor's degree or equivalent in Computer Science, related technical field or equivalent practical experience.
  • Programming experience in one or more of the following: Java, C, C++ and/or Python.
  • Experience in Natural Language Processing, Computational Linguistics, Machine Learning, Large-Scale Data Management, Data Mining or Artificial Intelligence
  • Contribution to research communities and/or efforts, including publishing papers at conferences such as ACL, EMNLP, NIPS, AAAI, ICML, SIGMOD, VLDB, and KDD.

Preferred

  • PhD in Computer Science, related technical field or equivalent practical experience.
  • Relevant work experience, including experience working within the industry or as a researcher in a lab.
  • Ability to design and execute on research agenda.
  • Strong publication record.


PhD-level Researchers, AIPHES, Darmstadt/Heidelberg

PhD positions in DFG Graduate School AIPHES: Natural Language Processing and Computational Linguistics

The Research Training Group “Adaptive Information Preparation from Heterogeneous Sources” (AIPHES), which has been established in 2015 at Technische Universität Darmstadt and at Ruprecht Karls University Heidelberg is filling several positions for three years, starting as soon as possible. Positions remain open until filled.

The positions provide the opportunity to obtain a doctoral degree in the research area of the training group with an emphasis, e.g., in opinion and sentiment - extrapropositional aspects of discourse, in natural language processing tasks such as structured summaries of complex contents, in content selection and classification enhanced by reasoning, or a related area. The group will be located in Darmstadt and Heidelberg. The funding follows the guidelines of the DFG, and the positions are paid according to the E13 public service pay scale.

The goal of AIPHES is to conduct innovative research in knowledge acquisition on the Web in a cross-disciplinary context. To that end, methods in computational linguistics, natural language processing, machine learning, network analysis, computer vision, and automated quality assessment will be developed. AIPHES will investigate a novel, complex scenario for information preparation from heterogeneous sources. It interacts closely with end users who prepare textual documents in an online editorial office, and who should therefore profit from the results of AIPHES. In-depth knowledge in one of the above areas is desirable but not a prerequisite.

Participating research groups at Technische Universität Darmstadt are Knowledge Engineering (Prof. Fürnkranz), Ubiquitous Knowledge Processing (Prof. Gurevych), Machine Learning (Prof. Kersting), Visual Inference (Prof. Roth), Algorithmics (Prof. Weihe). Participants at Ruprecht Karls University Heidelberg are the Institute for Computational Linguistics (Prof. Frank) and the Natural Language Processing Group (Prof. Strube) of the Heidelberg Institute for Theoretical Studies (HITS).

AIPHES emphasizes close contact between the students and their advisors with regular joint meetings, a co-supervision by professors and younger scientists in the research groups, and an intensive exchange as part of the research and qualification program. The training group has the goal of publishing its results at leading scientific conferences and will actively support its doctoral researchers in this endeavor. The software that will be developed in the course of AIPHES should be put under the open source Apache Software License 2.0 if possible. Moreover, the research papers and datasets should be published with open access models.


Prerequisites

We are looking for exceptionally qualified candidates with a degree in Computer Science, Computational Linguistics, or a related study program. We expect ability to work independently, personal commitment, team and communication abilities, as well as the willingness to cooperate in a multi-disciplinary team. Desirable is experience in scientific work. Applicants should be willing to work with German-language texts, and, if necessary, to acquire German language skills during the training program. We specifically invite applications of women. Among those equally qualified, handicapped applicants will receive preferential consideration. International applications are particularly encouraged.

The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. The Institute for Computational Linguistics (ICL) of the Ruprecht Karls University Heidelberg is one of the largest centers for computational linguistics both in Germany and internationally. The ICL and the NLP department of the HITS jointly run the graduate program “Semantic Processing” with an integrated research training group “Coherence in language processing: Semantics beyond the sentence”, which has a close connection to the topics in computational linguistics of AIPHES.

Applications should include a motivational letter that refers to one or two of the planned research areas of AIPHES, a CV with information about the applicant’s scientific work, certifications of study and work experience, as well as a thesis or other publications in electronic form. Application materials must be submitted via the following form by February 11th, 2018:

https://public.ukp.informatik.tu-darmstadt.de/aiphesrecruitment/

In addition, applicants should be prepared to solve a programming and a reviewing task in the first two weeks after their application.


Associate Research Scientist, UKP Lab, TU Darmstadt

The Ubiquitous Knowledge Processing (UKP) Lab at the Department of Computer Science, Technische Universität (TU) Darmstadt, Germany has an opening for an

Associate Research Scientist (PostDoc- or PhD-level; for an initial term of two years)

in the areas of Interactive Text Analysis, the UKP Lab is looking for a researcher with a background in Natural Language Processing and Software Development to work on the project INCEpTION funded by the German Research Foundation (DFG). The project is developing a comprehensive interactive text analysis platform to improve efficiency and to enable new ways of exploring, annotating and analyzing large-scale text corpora through the use of assistive features based on machine-learning.

We ask for applications from candidates from Computer Science with a specialization in Natural Language Processing, Text Mining, or Machine Learning, preferably with expertise in research and development projects, and strong communication skills. The successful applicant will work on research and development activities regarding text annotation by end-users (researchers, analysts, etc.), information recommendation, and create the corresponding text analysis platform. Ideally, the candidates should have demonstrable experience in designing complex (NLP and/or ML) systems (frontend and backend), in applying NLP-related Machine Learning-based methods, and strong programming skills especially in Java. Experience with neural network architectures and demonstrable engagement in open source projects are strong pluses.

The UKP Lab is a research group comprising over 30 team members who work on various aspects of Natural Language Processing (NLP), with a rapidly developing focus on Interactive Machine Learning and who provide a range of high-quality open source software packages for interactive and automatic text analysis to research and industry communities.

UKP’s wide cooperation network both within its own research community and with partners from research and industry provides an excellent work environment. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. Its Research Training Group “Adaptive Information Processing of Heterogeneous Content” (AIPHES) funded by the DFG emphasizes NLP, machine learning, text mining, as well as scalable infrastructures for the assessment and aggregation of knowledge. UKP Lab is a highly dynamic research group committed to high-quality research results, technologies of the highest standards, cooperative work style and close interaction of team members.

Applications should include a detailed CV, a motivation letter and an outline of previous working or research experience (if available).

Applications from women are particularly encouraged. All other things being equal, candidates with disabilities will be given preference. Please send the applications to: jobs@ukp.informatik.tu-darmstadt.de by 16.2.2018. The positions are open until filled. Later applications may be considered if the position is still open.

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