Employment opportunities, postdoctoral positions, summer jobs

From ACL Wiki
Revision as of 07:05, 1 November 2017 by Sien (talk | contribs) (Research position announcement)
Jump to navigation Jump to search

KU Leuven, Belgium : Researcher in Automated Reading of Documents

  • KU Leuven, Belgium: Postdoc or junior researcher in Automated Reading of Documents
  • Employer: KU Leuven, Belgium
  • Title: Postdoctoral or research fellow
  • Specialty: Machine Learning and Natural Language Processing

Location: Leuven, Belgium Deadline: Ongoing, desired start date: as soon as possible Date posted: November 1, 2017 Contact: Prof. Marie-Francine Moens


Researcher in Automated Reading of Documents
(Department of Computer Science, KU Leuven, Belgium)

The Language Intelligence & Information Retrieval lab (https://liir.cs.kuleuven.be) that is part of the Human Computer Interaction group of the Department of Computer Science of KU Leuven in Belgium has an open position for a motivated researcher interested in the latest developments in artificial intelligence for the automated reading of documents.

The research is carried out in the frame of the SaaS project (Self-learning SaaS platform for simplification of data-intensive customer experiences). The goal is to design, develop and test novel machine learning models that are self-learning and that can be applied for real-time processing of unstructured or semi-structured documents. Special attention will go to deep learning models relying on character-based or word-based representations of content.

We offer a research position in a research team that has an outstanding international reputation in natural language processing and understanding, multimedia mining, machine learning and information retrieval. Within the team we study both theoretical modelling and challenging applications. We investigate probabilistic graphical and deep learning models, with a special focus on learning with limited supervision. We have 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, user generated content mining, and web mining and search. KU Leuven is located about 25 kilometers from Brussels, the capital of Europe. For the second year in a row, KU Leuven leads the Reuters ranking as Europe’s most innovative university.


Required

  • Ph.D. in Computer Science, Artificial Intelligence, or a related field.
  • Research experience in machine learning.

Desired

  • Good knowledge of the English language and some knowledge of French or Dutch.

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: as soon as possible.
  • Competitive salary.

How to Apply
If interested, send your CV and motivation letter to Prof. Marie-Francine Moens (sien.moens@cs.kuleuven.be). The position will be filled in as soon as possible.


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 Spring/Summer 2018
  • Date posted: October 31, 2017
  • 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 Spring or Summer 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 Spring 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.

Two Postdoctoral Positions on Interpretable Vector Space Models

  • Employer: Cardiff University
  • Title: Postdoctoral research associate
  • Speciality: Neural networks, statistical relational learning, natural language processing
  • Location: Cardiff, UK
  • Deadline: November 2 2017
  • Date posted: October 6, 2017
  • Contact: Steven Schockaert

Applications are invited for two postdoctoral research posts at Cardiff University’s School of Computer Science & Informatics in the context of Steven Schockaert's FLEXILOG project, which is funded by the European Research Council (ERC). The overall aims of this project are (i) to learn interpretable vector space representations of entities and their relationships, and (ii) to exploit these vector space representations for various forms of flexible reasoning with, and learning from structured data. More information about FLEXILOG can be found on the project website: http://www.cs.cf.ac.uk/flexilog/

The aim of these positions will be to contribute to one or more of the following topics.

1) Learning structured event embeddings. In contrast to existing approaches, the learned embeddings will explicitly model which entities participate in the events, how they are related, and how their relationships are affected by different events. This will require combining ideas from neural network models for event embedding (e.g. based on LSTMs) with cognitively inspired representations (e.g. based on the theory of conceptual spaces). Among others, the resulting model will allow us to uncover more intricate causal relationships, to generate supporting explanations for causal predictions, to incorporate prior knowledge, and to transfer learned knowledge between domains.

2) Combining statistical relational learning with vector space models of commonsense reasoning. Low-dimensional vector space representations can be used to identify plausible formulas that are missing from a given knowledge base, intuitively by applying a kind of similarity or analogy based reasoning. Statistical relational learning (SRL) can also be used to infer plausible formulas, but instead relies on modelling statistical dependencies among relational facts at the symbolic level. Unifying both methodologies will allow us to develop powerful inference methods that combine their complementary strengths, enabling interpretable and robust plausible reasoning from sparse relational data.

3) Geometric representations of logical theories. Most vector space models for knowledge base completion simply represent entities, attributes and relations as vectors. In many domains, however, plausible inferences rely on complex dependencies that cannot be captured by such representations. As an alternative, we will develop methods in which predicates are represented as regions, and logical formulas correspond to qualitative constraints on the spatial configurations of these regions. This model will support more complex inferences than existing approaches, will allow us to exploit existing domain knowledge when learning vector space representations, and will conversely allow us derive approximate logical theories from a learned embedding.

Cardiff University is a member of the Russell Group of research universities, and was ranked 5th in the UK based on the quality of research in the 2014 Research Evaluation Framework. The university has a successful School of Computer Science & Informatics with an international reputation for its teaching and research activities. Cardiff is a strong and vibrant capital city with good transportation links and an excellent range of housing available.

For more details about the positions, please contact Steven Schockaert (SchockaertS1@cardiff.ac.uk). For instructions on how to apply, please go to www.cardiff.ac.uk/jobs and search for job 6522BR. Please note the requirement to evidence all essential criteria in the supporting statement.

Salaried 4-year PhD Position in Computational Linguistics/NLP at Stockholm University

  • Employer: Stockholm University, Sweden
  • Title: PhD candidate
  • Speciality: Computational Linguistics/Natural Language Processing
  • Location: Stockholm, Sweden
  • Deadline: October 16, 2017
  • Date posted: September 20, 2017
  • Contact: Robert Östling

More information and application form: http://www.su.se/english/about/working-at-su/jobs?rmlang=UK&rmpage=job&rmjob=3869

The Department of Linguistics at Stockholm University is looking for a new PhD candidate in the area of computational linguistics/natural language processing. PhD candidates are regular employees of Stockholm University, with a starting salary of 25,300 SEK (2,650 EUR; 3,200 USD) per month and the same benefits and social security as other University employees. The position is fully funded for 4 years. Extension up to one year is possible if the candidate performs teaching or other duties at the department, and further extension is granted in case of parental or sick leave.

The choice of thesis topic is not restricted to a particular project, but should be aligned with the research profile of the department. Possible topics include multilingual NLP methods, machine translation, or computational methods for other areas of research at the department (language acquisition, linguistic typology, phonetics, sign language).

Potential applicants are encouraged to contact Robert Östling to discuss possible thesis projects, or other issues related to the position.

Tenure Line Assistant Professor Position in Linguistics at Northwestern University

  • Employer: Northwestern University, USA
  • Title: Tenure Line Assistant Professor Position in Linguistics at Northwestern University
  • Speciality: Meaning
  • Location: Evanston, IL, USA
  • Deadline: December 1, 2017
  • Date posted: September 18, 2017
  • Contact: matt-goldrick@northwestern.edu

http://www.linguistics.northwestern.edu/about/news/faculty-search.html

The Department of Linguistics at Northwestern University seeks to fill a tenure-line assistant professor position with a start date of September 1, 2018. We are looking for candidates with research and teaching interests in meaning, broadly construed. We are particularly interested in candidates whose research program includes cognitive, computational, and/or social approaches. The successful candidate will join a vibrant interdisciplinary community of researchers in the science of language, including computer science, philosophy, psychology, cognitive neuroscience, and speech science.

To receive fullest consideration, applications should be uploaded by December 1, 2017. Candidates must hold a Ph.D. in Linguistics, Cognitive Science, Computer Science, Psychology, or a related field by the start date. Please include a CV (including contact information), statements of research and teaching interests, reprints or other written work, teaching evaluations (if available), and the names of three references (with their contact information). References will separately receive upload instructions after you have submitted your application (letters of reference should arrive as close as December 1st as possible).

The Department is strongly committed to enhancing diversity, equity and inclusion in all aspects – including, but not limited to, race/ethnicity, and gender, as well as disability, sexual orientation, and gender expression and identity. We encourage applications from candidates that share this vision.

E-mail inquiries should be directed to Matt Goldrick, Chair.

Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes including veterans and individuals with disabilities. Women, racial and ethnic minorities, individuals with disabilities, and veterans are encouraged to apply. Hiring is contingent upon eligibility to work in the United States.

PhD-level Researchers, AIPHES, Darmstadt/Heidelberg

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 several positions for three years, starting on April 1st, 2018. Positions remain open until filled.

PhD-level Researchers in Natural Language Processing, Computational Linguistics, Machine Learning, or related areas

The positions provide the opportunity to obtain a doctoral degree in the research area of the training group with an emphasis, e.g., in graph-based discourse processing, in natural language processing tasks such as automated summarization, in representation and analysis of text-induced structures, in jointly analyzing text and images, or in 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 the 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, have 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 [http://www.cl.uni-heidelberg.de/ 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 should be submitted via the following form by October 6th, 2017: 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 on Sentence Understanding and Generation at NYU

  • Employer: New York University, Machine Learning for Language Group (Sam Bowman and Kyunghyun Cho)
  • Title: Postdoc
  • Specialty: Sentence understanding and generation using deep neural networks with latent tree structures or other latent variables
  • Location: New York, NY, USA
  • Deadline: Rolling
  • Date posted: September 15, 2017
  • Contact: Sam Bowman

The Machine Learning for Language Group at NYU expects to hire at least one postdoc to start some time in 2018, working with one or both of PIs Kyunghyun Cho and Sam Bowman.

We expect the researcher to use their time here to develop an independent research program which involves work on neural network models for natural language understanding or generation at the sentence level and to also participate in work on models which use latent tree structures or other continuous or discrete latent variables. The position will be funded through a sponsored research agreement on this topic, and while the researcher may be asked to contribute some effort to the completion of the sponsored research, this shouldn’t be a burden: It will only involve the development, evaluation and publication of novel modeling methods on public datasets.

For more details, see the full ad here:

https://wp.nyu.edu/ml2/postdoc-opening/

PhD Position on Adaptive Text Generation in a Serious Game, The Netherlands

  • Employer: University of Twente
  • Title: PhD position
  • Specialty: Natural Language Generation
  • Location: Enschede, The Netherlands
  • Deadline: 28 August, 2017
  • Date posted: August 4, 2017
  • Contact: Mariët Theune

The research group Human Media Interaction of the University of Twente is looking for a PhD candidate to work on adaptive text generation. The PhD research aims at generating natural language texts in Dutch, for use in a training game for Dutch firefighters. The texts will be adaptive in the sense that they will be tailored to the player’s individual needs and competences. The type of narrative game that will be developed in our project (in collaboration with a serious game company) offers multiple opportunities for such tailoring of textual game content. Specifically, the goal is to work on the generation of in-game texts based on current real-world data, the generation of non-player character dialogue and the generation of post-game narrative feedback on player performance. Since the generated texts will be in Dutch, the PhD candidate is expected to have a good command of the Dutch language.

The position is full-time for four years. Starting date is as soon as possible. Find more details about the position, including how to apply, by clicking on the link below:

https://www.utwente.nl/en/organization/careers/vacancies/!/vacature/1155511

Permanent Position for Postdocs in Machine Learning & NLP, Paris, France

  • Employer: SPARTED
  • Title: Project Researcher
  • Specialty: NLP, Machine Learning, Deep Learning, Information Extraction
  • Location: Paris (16), France
  • Deadline: Until candidate is found
  • Date posted: August 4, 2017
  • Contact: [1]; phone [+33] (06)52148693
  • Website: http://www.sparted.com

SPARTED is an innovative and disruptive French EdTech startup that is changing the way people learn by turning employees into daily learners. We offer companies and organizations a SaaS micro-learning mobile platform that allows them to create online gamified content and deliver it independently in a white label app. SPARTED is initiated an ambitious project and is hiring a Postdoc researcher to pilot it. Find more details about the position by clicking on the link below:

http://files.sparted.com/all/Job%20description/AI%20FICHE%20DE%20POSTE.pdf

Funded PhD Position in NLP & Music Technology, Universitat Pompeu Fabra, Barcelona, Spain

  • Employer: Universitat Pompeu Fabra [2], Barcelona, Spain
  • Title: PhD Scholarship
  • Specialty: Text Mining, Information Extraction, Music Information Retrieval
  • Location: Barcelona, Spain
  • Deadline: Until candidate is found
  • Date posted: June 10, 2017
  • Contact: [3]


PhD position on data-driven methodologies for music knowledge extraction In the context of a collaborative project between the Music Technology and the Natural Language Processing groups of the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF) we offer a PhD position dedicated to developing data-driven methodologies for music knowledge extraction by combining Natural Language Processing and Music Information Retrieval approaches.

Supervisors of the position: Xavier Serra and Horacio Saggion Contact for application: Aurelio Ruiz (aurelio.ruiz@upf.edu)

The work to be done in this PhD will aim at processing music related text from open web sources in order to generate musically relevant knowledge. For this, it will require combining methodologies coming from Music Information Retrieval (MIR), Natural Language Processing (NLP) and Computational Musicology.

The PhD position is part of the María de Maeztu Strategic Research Program on data-driven knowledge extraction (MDM-2015-0502) and linked to the program of the Spanish Ministry of Science and Competitiveness .


Scientific System Developer, 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 a

Scientific System Developer
(PostDoc- or PhD-level; time-limited project position until April 2020)

to strengthen the group’s profile in the area of Argument Mining, Machine Learning and Big Data Analysis. The UKP Lab is a research group comprising over 30 team members who work on various aspects of Natural Language Processing (NLP), of which Argument Mining is one of the rapidly developing focus areas in collaboration with industrial partners.

We ask for applications from candidates in Computer Science preferably with expertise in research and development projects, and strong communication skills in English and German. The successful applicant will work in projects including research activities in the area of Argument Mining (e.g. automatic evidence detection, decision support, large-scale web mining on heterogeneous source and data management), and development activities to create new products or industrial product prototypes. Prior work in the above areas is a definite advantage. Ideally, the candidates should have demonstrable experience in designing and implementing complex (NLP) systems in Java and Python as well as experience in information retrieval, large-scale data processing and machine learning. Experience with continuous system integration and testing and distributed/cluster computing is a strong plus. Combining fundamental NLP research with industrial applications from different application domains will be highly encouraged.

UKP’s wide cooperation network both within its own research community and with partners from industry provides an excellent environment for the position to be filled. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. Its unique and recently established Research Training Group “Adaptive Information Processing of Heterogeneous Content” (AIPHES) funded by the DFG emphasizes NLP, text mining, machine learning, 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 industrial standards, cooperative work style and close interaction of team members working on common goals.

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 application to: jobs@ukp.informatik.tu-darmstadt.de by 31.05.2017. The position is open until filled. Later applications may be considered if the position is still open.

Questions about the position can be directed to: Johannes Daxenberger; phone: [+49] (0)6151 16-25297 We look forward to receiving your application!


Two postdoc positions on plausible reasoning with vector space embeddings at Cardiff University

  • Employer: Cardiff University
  • Title: Postdoctoral Research Associate
  • Specialty: vector space embeddings, statistical relational learning, knowledge representation, neural networks, explainable AI
  • Location: Cardiff, UK
  • Deadline: May 20, 2017
  • Date posted: April 20, 2017
  • Contact: Steven Schockaert

Applications are invited for two Postdoctoral Research Associate posts at Cardiff University’s School of Computer Science & Informatics:

  • The focus of the first position will be on developing methods for exploiting entity embeddings in statistical relational learning, to enable robust plausible reasoning from sparse relational data. Entity embeddings can be used to identify plausible formulas that are missing from a given knowledge base, intuitively by applying a kind of similarity or analogy based reasoning. Statistical relational learning can also be used to infer plausible formulas, but instead relies on modelling statistical dependencies among relational facts at the symbolic level. Unifying both methodologies will allow us to develop powerful inference methods that combine their complementary strengths. The resulting method will be applied to zero and one shot learning tasks, with a focus on automated knowledge base completion.
  • The focus of the second position will be on learning vector space embeddings of events and the causal relations between them. In contrast to existing approaches, the learned embeddings will explicitly model which entities participate in the events, how they are related, and how their relationships are affected by different events. This will require combining ideas from neural network models for event embedding (e.g. based on LSTMs) with ideas from knowledge graph embedding models. Among others, the resulting model will allow us to uncover more intricate causal relationships, to generate supporting explanations for causal predictions, to incorporate prior knowledge, and to transfer learned knowledge between domains. Intended applications include recognising textual entailment, stock market prediction, and event-focused information retrieval.

Successful candidates are expected to have a strong background in natural language processing, machine learning, or knowledge representation. This research will be part of Steven Schockaert's FLEXILOG project, which is funded by the European Research Council (ERC)

Cardiff University is a member of the Russell Group of research universities, and was ranked 5th in the UK based on the quality of research in the 2014 Research Evaluation Framework. The university has a successful School of Computer Science & Informatics with an international reputation for its teaching and research activities. Cardiff is a strong and vibrant capital city with good transportation links and an excellent range of housing available.


More information For more details about the positions, please contact Steven Schockaert (SchockaertS1@cardiff.ac.uk). For instructions on how to apply, please go to www.cardiff.ac.uk/jobs and search for job 5878BR. Please note the requirement to evidence all essential criteria in the supporting statement.


Postdoc in Machine Learning for Multimodal Behavior and Mental State Analysis, University of Colorado Boulder

  • Employer: University of Colorado Boulder
  • Title: Postdoctoral Research Associate
  • Specialty: Advanced Machine Learning
  • Location: Boulder, Colorado, United States
  • Deadline: Ongoing, desired start Summer/Fall 2017
  • Date posted: March 31, 2017
  • Contact: Dr. Sidney D’Mello

Postdoc in Machine Learning for Multimodal Behavior and Mental State Analysis
(Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder)

The Institute of Cognitive Science (ICS) and Department of Computer Science at the University of Colorado Boulder anticipates hiring a full time postdoctoral fellow starting Summer/Fall 2017 for one year and renewable for a second 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 postdoc will develop and apply machine learning techniques in the hierarchical and temporal domains to model behavioral and mental states (e.g., affect, attention, workload) from multimodal data (e.g., video, audio, physiology, eye gaze) across a range of interaction contexts (e.g., online learning, in-class learning, 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, Cognitive Science, and Education.

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 advanced 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 for temporal and hierarchical domains (e.g., probabilistic graphical models, deep recurrent neural networks) applied to human behavior and mental state analysis (e.g., affective computing, dyadic/triadic interaction)
  • Self-motivated with a strong work ethic and writing proficiency as evidenced by a strong publication record

Desired

  • Research experience in one or more of the following areas (computer vision, eye tracking, computational psychophysiology, fMRI, multimodal fusion, collaborative problem solving, real-world sensing)
  • Experience mentoring graduate and undergraduate students

Job Details

  • 1-2 year position. Initial contract is for one year (providing renewal after 6-month probationary period). Second year contract is based on performance and availability of funds.
  • Start date is negotiable, but anticipated for Summer/Fall 2017.
  • Competitive salary with benefits commensurate with qualifications. This position is eligible for medical, dental and life insurance, retirement benefits programs, and is eligible for monthly vacation and sick leave accruals.

How to apply
Please complete Faculty/University Staff EEO Data (application) form (https://goo.gl/YC9g94) and upload the following required documents: 1—Cover letter; 2—Curriculum Vitae 3—List of Three References 4-One or two representative publications.

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.

Questions
Please email Dr. Sidney D’Mello


Researcher in Machine Learning and NLP, DFKI, Germany

  • Employer: DFKI GmbH, Germany
  • Title: Researcher
  • Specialty: Machine Learning and Natural Language Processing, Deep Learning, Machine Translation
  • Location: Saarbruecken
  • Deadline: March 31, 2017
  • Date posted: March 13, 2017
  • Contact: Prof. Josef van Genabith

The Multilingual Technologies (MLT) Lab at DFKI is looking to expand its expertise in Machine Learning with a focus on Deep Learning, Machine Translation and possibly other areas of NLP. Depending on experience, the position is available at the Junior/Researcher/Senior/Principal Researcher level.

Key research responsibilities include:

  • machine and deep learning for natural language processing/machine translation
  • software development and integration
  • publication in top-tier conferences and journals

General responsibilities include:

  • engagement with industry partners and contract research
  • identification of funding opportunities and engagement in proposal writing
  • contribution to teaching and supervision in accordance with University and DFKI rules and regulations
  • administrative work associated with programmes of research

Requirements:

  • MSc/PhD in computer science, machine learning, natural language processing, computational linguistics or similar
  • Strong background and track record in machine learning, neural nets and deep learning
  • Strong background and track record in NLP and MT - Excellent programming skills
  • Excellent problem solving skills, independent and creative thinking
  • Excellent team working and communication skills
  • Excellent command of written and oral English
  • Command of German and other languages not a requirement but helpful

The successful applicant will work in the DFKI MLT lab led by Prof. Josef van Genabith (Scientific Director MLT, DFKI, and Chair of Translation-Oriented Language Technologies, Saarland University).

Working environment: DFKI is one of the largest AI research institutes worldwide, with several sites in Germany, covering basic research and applications. DFKI is a not-for-profit company with more than 500 researchers from 60+ countries across the globe. DFKI is based on a shareholder model including globally operating companies such as Intel, Google, Microsoft, Nuance, SAP, BMW, VW, Bosch, Deutsche Telekom, several SMEs, three German universities and three German Federal States.

The DFKI Multilingual Technologies lab partners in international, national and industry funded research projects in all areas of Language Technologies (including machine translation, question answering, information extraction, human-robot communication, speech and the multi-lingual web). The MLT lab currently leads the H2020 European Research project QT21 on MT, the EU CEF funded ELRC project and the EU funded TRADR project on human-robot collaboration in disaster response scenarios.

The MLT lab is part of the DFKI site at the Saarland University campus in Saarbrücken, Germany. Saarland University has exceptionally strong Computer Science and Computational Linguistics departments, two Max Plank Institutes in Computer Science, an Excellence Cluster in Multimodal Computing and Interaction and several International Doctoral and Master programmes in Computer Science and Computational Linguistics. DFKI staff regularly engage in teaching and supervision at Saarland University.

Geographical environment: Saarbrücken is the capital of Saarland with approximately 190,000 inhabitants. It is located right in the heart of Europe and is the cultural center of this border region of Germany, France and Luxembourg. Some of the closest larger cities are Trier, Nancy, Mannheim, Karlsruhe and Frankfurt. Paris can be reached by train in just under 2 hours. Living costs are modest in comparison with other large cities in Germany and elsewhere in Europe.

Starting date, duration, salary: Preferred starting date is May/June 2017. The position is available until June 30, 2020, with opportunities for extension depending on performance and future funding. Compensation is competitive and reflects individual competence, seniority and special skills.

Application: Applications are required to include a short cover letter, a CV, list of publications, a brief summary of research interests, and contact information for three references. Please send your electronic application (preferably in PDF format) to Prof. Josef van Genabith referring to job opening no. 22/17-JvG. Deadline for applications is March 31st, 2017. The position remains open until filled. Please contact Prof. van Genabith for informal inquiries.


Associate Research Scientist, UKP Lab, TU Darmstadt

  • Employer: UKP Lab, Technische Universität Darmstadt, Germany
  • Title: Associate Research Scientist
  • Specialty: Interactive Machine Learning (IML) or Natural Language Processing for Language Learning
  • Location: Darmstadt
  • Deadline: March 8, 2017
  • Date posted: February 21, 2017
  • Contact: Prof. Iryna Gurevych

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

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

to strengthen the group’s profile in the areas of Interactive Machine Learning (IML) or Natural Language Processing for Language Learning. The UKP Lab is a research group comprising over 30 team members who work on various aspects of Natural Language Processing (NLP), of which Interactive Machine Learning and Natural Language Processing for Language Learning are the focus areas researched in collaboration with partners in research and industry.

We ask for applications from candidates in Computer Science with a specialization in Machine Learning or Natural Language Processing, preferably with expertise in research and development projects, and strong communication skills in English and German.

  • The successful applicant in the area of Interactive Machine Learning will work on research activities regarding its application to end-user content annotation, information structuring and recommendation, or semantic text analysis, and development activities to create functional and attractive user-oriented product prototypes.
  • The successful applicant in the area of Natural Language Processing for Language Learning will work on research activities in automatically assessing language competencies and readability as well as on generating exercise material for language learners in intelligent real-time learning systems.

Prior work in the above areas is a definite advantage. Ideally, the candidates should have demonstrable experience in designing and implementing complex (NLP and/or ML) systems, experience in large-scale data analysis, large-scale knowledge bases, and strong programming skills incl. Java. Experience with neural network architectures and a sense for user experience design are a strong plus. Combining fundamental NLP research on Interactive Machine Learning or Natural Language Processing with practical applications in different domains including education will be highly encouraged.

UKP’s wide cooperation network both within its own research community and with partners from research and industry provides an excellent environment for the position to be filled. The Department of Computer Science of TU Darmstadt is regularly ranked among the top ones in respective rankings of German universities. Its unique research initiative "Knowledge Discovery in the Web" and the Research Training Group "Adaptive Information Processing of Heterogeneous Content" (AIPHES) funded by the DFG emphasize 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 industrial standards, cooperative work style and close interaction of team members working on common goals.

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 08.03.2017. The positions are open until filled. Later applications may be considered if the position is still open.

Postdoctoral Fellowship in Linguistics and Cognitive Science at Northwestern University

  • Employer: Northwestern University, USA
  • Title: Postdoctoral Fellowship in Linguistics and Cognitive Science at Northwestern University
  • Speciality: Open area
  • Location: Evanston, IL, USA
  • Deadline: April 1, 2017
  • Date posted: February 17, 2017
  • Contact: matt-goldrick@northwestern.edu

The Department of Linguistics at Northwestern University invites applications for a full-time, non-renewable, two year postdoctoral fellowship in any area of linguistics. We are looking for candidates who pursue an integrated, interdisciplinary approach to the scientific study of language, utilizing experimental methods, corpus analysis, and/or computational modeling to inform linguistic theory and its applications. The fellowship period begins September 1, 2017. Each year, the fellow will be expected to teach one undergraduate-level course in the Department of Linguistics. The fellow will also serve as an undergraduate adviser for the Cognitive Science Program, working with students pursuing the major and minor on academic issues (e.g., course selection, research opportunities, progress on degree requirements).

The fellow will join a vibrant interdisciplinary community of researchers from across the cognitive sciences (including communication sciences, computer science, learning sciences, music cognition, neuroscience, philosophy, and psychology). The fellow’s research will be supported by the facilities of the Department of Linguistics.

To receive fullest consideration, applications should arrive by April 1, 2017. Candidates must hold a Ph.D. in Linguistics or a related field (e.g., Cognitive Neuroscience, Cognitive Science, Computer Science, Philosophy, Psychology, Speech and Hearing Sciences) by the start date. Please include a CV that includes contact information, brief statements of research and teaching interests (1-3 pages each), up to 3 reprints or other written work (including thesis chapters for ABD applicants), teaching evaluations (if available), and the names and contact information for three references. Please visit http://www.linguistics.northwestern.edu/ for online application instructions.

E-mail inquiries should be directed to Matt Goldrick, Chair of the Department of Linguistics (matt-goldrick@northwestern.edu). Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes including veterans and individuals with disabilities. Women and minorities are encouraged to apply. Hiring is contingent upon eligibility to work in the United States.

Postdoctoral Research Position in Interpretable Machine Learning at Cardiff University, UK

  • Employer: Cardiff University, UK
  • Title: Research Associate in Artificial Intelligence / Machine Learning
  • Speciality: Vector Space Models, Deep Learning, Interpretable Machine Learning, Symbolic Models
  • Location: Cardiff, UK
  • Deadline: March 2, 2017
  • Date posted: February 13, 2017
  • Contact: schockaerts1@cardiff.ac.uk

Applications are invited for a Postdoctoral Research Associate post in Cardiff University’s School of Computer Science & Informatics. This is a full-time, fixed-term post for 30 months, starting on 1 May 2017 or as soon as possible thereafter. The successful candidate will be dedicated to finding creative solutions and have a genuine curiosity and enthusiasm to undertake world-class research in the field of Machine Learning / Artificial Intelligence. Specifically, the aim of this post will be to develop novel methods for learning interpretable/symbolic models from diverse sources of information, including knowledge graphs, vector space models and natural language text. These models will then be used as background theories in applications such as recognising textual entailment, automated knowledge base completion, or zero-shot learning. You will work closely with Steven Schockaert. You will possess or be near the completion of a PhD in Computer Science or a related area, or have relevant industrial experience.

This research will be part of the FLEXILOG project, which is funded by the European Research Council (ERC)

Essential criteria

  • Postgraduate degree at PhD level, or near to completion of a PhD in a related subject area or relevant industrial experience
  • An established expertise and proven portfolio of research and/or relevant industrial experience within at least two of the following research fields: Machine Learning, Knowledge Representation, Natural Language Processing.
  • A strong background in statistics and linear algebra.
  • Excellent programming skills.
  • Knowledge of current status of research in specialist field.
  • Proven ability to publish in relevant journals (e.g. Artificial Intelligence, Journal of Artificial Intelligence Research, Journal of Machine Learning Research, Machine Learning) or top-tier conferences (e.g. IJCAI, AAAI, ECAI, NIPS, ICML, KDD, ACL, EMNLP).
  • Ability to understand and apply for competitive research funding.
  • Proven ability in effective and persuasive communication.
  • Ability to supervise the work of others to focus team efforts and motivate individuals.
  • Proven ability to demonstrate creativity, innovation and team-working within work.

Background about the university

Cardiff is a strong and vibrant capital city with good transportation links and an excellent range of housing available. Various surveys have ranked it as one of the most liveable cities in Europe. Cardiff University is a member of the Russell Group of research universities, and was ranked 5th in the UK based on the quality of research in the 2014 Research Evaluation Framework. The university has a successful School of Computer Science & Informatics with an international reputation for its teaching and research activities. The school has a strong research track record recognised for its outstanding impact in terms of reach and significance, with 79% of its outputs deemed world-leading or internationally excellent in the 2014 Research Excellence Framework.

Background about the project

Vector space embeddings have become a popular representation framework in many areas of natural language processing and knowledge representation. In the context of knowledge base completion, for example, their ability to capture important statistical dependencies in relational data has proven remarkably powerful. These vector space models, however, are typically not interpretable, which can be problematic for at least two reasons. First, in applications it is often important that we can provide an intuitive justification to the end user as to why a given statement is believed, and such justifications are moreover invaluable for debugging or assessing the performance of a system. Second, the black box nature of these representations makes it difficult to integrate them with other sources of information, such as statements derived from natural language, or from structured domain theories. Symbolic representations, on the other hand, are easy to interpret, but classical inference is not sufficiently robust (e.g. in case of inconsistency) and too inflexible (e.g. in case of missing knowledge) for most applications.

The overall aim of the FLEXILOG project is to develop novel forms of reasoning that combine the transparency of logical methods with the flexibility and robustness of vector space representations. For example, symbolic inference can be augmented with inductive reasoning patterns (based on cognitive models of human commonsense reasoning), by relying on fine-grained semantic relationships that are derived from vector space representations. Conversely, logical formulas can be interpreted as spatial constraints on vector space representations. This duality between logical theories and vector space representations opens up various new possibilities for learning interpretable domain theories from data, which will enable new ways of tackling applications such as recognising textual entailment, automated knowledge base completion, or zero-shot learning.

More information

For more details about the project and instructions on how to apply, please go to www.cardiff.ac.uk/jobs and search for job 5545BR. Please note the requirement to evidence all essential criteria in the supporting statement.

Research Associates in Natural Language Processing / Text Mining, University of Manchester, UK

  • Employer: National Centre for Text Mining (NaCTeM), School of Computer Science, University of Manchester, UK
  • Title: Research Associates in Natural Language Processing / Text Mining
  • Speciality: Natural Language Processing, Text Mining
  • Location: Manchester, UK
  • Deadline: March 13, 2017
  • Date posted: February 10, 2017
  • Contact: sophia.ananiadou@manchester.ac.uk

The School of Computer Science, National Centre for Text Mining at the University of Manchester seeks to appoint two Research Associates in Natural Language Processing-based Text Mining to expand its text mining research portfolio.

They will join a strong team of 12+ staff who work on numerous national and international research projects, including industry, in areas of information extraction, disambiguation, topic analysis, natural language processing, biomedical text mining and machine learning.

Skills

You should have a PhD in Computer Science with an emphasis on Natural Language Processing and Text Mining. The focus of your research will be in developing (semi)-supervised methods for information extraction, in particular relation, event extraction and normalisation; a proven ability to develop algorithms for NLP/text mining problems using deep learning will be highly desirable; knowledge of developing text mining workflows using UIMA based environment will be a plus. You should have excellent programming skills, preferably in Java.

  • Duration of post: Immediately until 31st October 2018
  • Salary: £31,076-£38,183 per annum

Research Team

The National Centre for Text Mining (http://www.nactem.ac.uk) has been a leading centre for text mining since 2004, with areas of expertise in information extraction, terminology, text classification, text mining infrastructures and semantic search systems. NaCTeM is located in the Manchester Institute of Biotechnology (http://www.mib.ac.uk) and its staff belong to the 4th ranked Computer Science school in the UK (REF2014) which has been further assessed as having the "best environment in the UK for computer science and informatics research”.

Informal enquiries: Prof. Sophia Ananiadou (Sophia.ananiadou@manchester.ac.uk).

Deadline of applications: 13/03/2017

Application forms and further particulars: https://www.jobs.manchester.ac.uk/displayjob.aspx?jobid=12975

Retrieved from "https://aclweb.org/aclwiki/index.php?title=Employment_opportunities,_postdoctoral_positions,_summer_jobs&oldid=12026"