Researcher Job in Natural Language Processing / Data science is available for doctoral degree candidates in language technology/natural language processing, computer science, complex systems, mathematics, linguistics with experience in computational methods, or informational sciences at the Department of Swedish, Multilinguality, language technology, University of Gothenburg, Sweden 2022
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No. of Positions: 1
Research Field: Computer Science, Information Sciences (Studies), Linguistics, Mathematics, Natural language processing (NLP)
Deadline to Apply: Expired
Joining Date: ASAP
Contract Period: Subject to Norms
Salary: According to standard norms
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To be eligible as a postdoctoral fellow one must have completed a doctoral degree or who has a foreign degree that is deemed to correspond to a completed doctoral degree. This eligibility requirement must be met at the latest at the time when the employment decision is made. The doctoral thesis should be in a relevant area according to the specific position stated here (for example, language technology/natural language processing, computer science, complex systems, mathematics, linguistics with experience in computational methods, or informational sciences). Since the postdoctoral researcher position is designed to give new PhDs the opportunity to strengthen and develop their scholarly proficiency, priority will be given to those whose doctoral degree was granted within 3 years of the application closing date. Due to parental leave, sick leave or military duty, this period can be extended.
Eligibility criteria are described in the Gothenburg University's Appointments procedure for teachers: 1803446_appointment-procedure-gu-2022-38-eng-2022-02-17.pdf
The assessment of the application will be based mainly on scientific skills. The main considerations in ranking candidates will be quality of their scientific track record and how central their previous research is to the research methods relevant to the project and program goals. Previous work on LSC and other NLP oriented research topics is an advantage, but not a requirement. You are expected to provide evidence of excellence in research. In the assessment of scientific proficiency, special attention will be paid to researchers whose research and previous experience strengthens the research group and projects specified in this call. A high degree of independence, and critical thinking are also expected, as well as regularly publishing in highly ranked conferences such as ACL, EACL, EMNLP, alternatively, NeurIPS. The applicant should have documented experience and skill in driving their own research with relevance for the two research projects, in working both independently and in group. Excellent writing skills in English is a must.
As a secondary criterion, experience of modern machine learning frameworks (such PyTorch and/or Tensorflow/Keras) and large scale methods for modeling language in Python will be considered a strong merit. Experiences with methods for modeling LSC, work with historical or modern large-scale textual corpora is a plus, but not a requirement.
We value a strong mathematical and scientific background and experience with computational models of language as well as data science investigations of large-scale data. Previous experience with modern NLP tools and language models, such as transformers, Word2Vec, and topic models, as well as general machine learning methods is meritious. Proficiency in other mathematical and computational methods is an advantage.
Considerable weight will be accorded to personal skills such as being:
- highly independent and self-motivated
- flexible and adaptable to changing circumstances.
- responsible, working in a structured way, and following time plans.
- good at taking initiative, and getting started and producing results.
- good at communicating research and results such that other people understand, both laymen and researchers from other fields.
The recruitment process will consist of an interview (most likely remotely over video link) during which we will discuss how your research and experiences can contribute to computational modeling for semantic change (see https://langsci-press.org/catalog/view/303/3027/2374-1 for a survey on the topic), and a work sample in the form of a programming task or other problem solving task related to the call.
The position focus: identification and analysis of lexical semantic change using computational models applied to diachronic texts. Our languages change over time. As a consequence, words may look the same, but have different meanings at different points in time, a phenomenon called lexical semantic change (LSC). If we do not know which words have changed their meaning and in what ways, we run into problems when interpreting older texts (or more recent texts from different contexts). To fascilitate interpretation, search, and analysis of old texts we build computational methods for automatic detection and characterization of LSC from large amounts of text. Our outputs would be directly applicable to the lexicographic R&D unit in our department that compiles the Swedish Academy dictionaries, as well as to researchers from historical linguistics, analytical sociology, gender studies, conceptual history, literary studies, and other research disciplines in the humanities and social sciences that include textual analysis as a central methodological component.
The research is to be conducted in two research projects with closely related themes involving computational modeling of lexical semantic change. Prior knowledge in historical linguistics or computational methods for LSC is not required. However, a genuine interest to learn and investigate these domains, as well as proven experience in using computational tools to study a range of domains outside the realms of NLP will be highly valued.
The major part of the work (75%) will be carried out within “Change is Key!”, a research program funded by Riksbankens Jubileumsfond (RJ), where SBX is the coordinating partner. Here, you will conduct research in automatic detection and analysis of LSC from diachronic texts specifically targeted towards change over multiple (hundreds or more) points in time. The Change is Key! Program offers a vibrant research environment for this exciting and rapidly growing cutting-edge research field in NLP. There is a unique opportunity to contribute to the field of LSC, but also to humanities and social sciences through our active collaboration with international researchers in historical linguistics, analytical sociology, gender studies, conceptual history, and literary studies.
The remainder of the research (25%) is to be conducted within the research project “Towards computational lexical semantic change detection” (funded by a grant from the Swedish Research Council), where SBX is the coordinating partner. The successful applicant will conduct research in this project targeted towards automatic detection and analysis of LSC from diachronic texts. More information regarding the project can be found on www.languagechange.org. Both project and program are seamlessly integrated with a shared research group and joint research meetings.
This postdoc position will offer the opportunity to strengthen and enhance scientific skills by conducting research with focus on computational modeling of semantic change together with the researchers at SBX, as well as international researchers from KULeuven (Belgium), IMS Stuttgart (Germany), and Queen Mary University of London (UK). A 3-to-6 month research stay at one of our partner institutions is possible
The successful applicant will conduct research in NLP, and will develop methods for modeling lexical semantic change for historical texts, with a particular focus on multiple-time point change detection. In particular, deep learning language models and general machine learning methods should be used to develop models for semantic change detection and analysis using large collections of (historical) texts across hundreds of points in time. It is also encouraged to pursue these targets with other methods (e.g., graph theoretical perspectives, modeling of complex (social) networks). The results should be evaluated on English and Swedish document collections, on evaluation sets such as SemEval tasks for word sense induction, and unsupervised semantic change detection, and other existing change testsets. The successful applicant will also help identify and develop relevant and new testsets. If time permits, the postdoc will use the developed models to investigate qualitative hypotheses posted by researchers from history, linguistics, and social sciences. The research tasks will be defined together with the project/program leader to correspond to the project goals, and conducted in an active research environment where in-depth knowledge of methods and evaluation for lexical semantic change exist.
We collaborate by regular meetings in which you will take an active part in the discussions and present results. The postdoc work should contribute to scientific progress which is presented in jointly published journal papers and national and international conference papers.
Apart from research, duties may include teaching-related activities; such supplementary duties will not exceed 15% of the position’s responsibilities and be included in the Change is Key! time.
The holder of the position is expected to carry out the vast majority of the research in Gothenburg and to actively participate in Språkbanken Text’s workshops, seminars, and conferences.
How to Apply?
Application Method: Online Application
Ref. No.: PAR 2021/1502
In order to apply for a position at the University of Gothenburg, you have to register an account in our online recruitment system. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline. The selection of candidates is made on the basis of the qualifications registered in the application. If you have any problems or questions, regarding the recruitment system, please contact [email protected]
The application must include:
- curriculum vitae (CV) of both scientific and pedagogical work
- list of degrees and previous employments
- a full list of scientific papers (Attach phd thesis and possibly other published writings, at most 7 works to the application.)
- other documents you wish to refer to track and that can have significant impact on the assessment
- a copy of the doctoral degree
- a brief statement (maximum 1 page) about how your previous research and experiences relate to the project and program.
- Contact details for two references
More information is available at: 1771040_anvisningar-ovrig-larare-ansokan-180402-eng.pdf (gu.se)
We are happy to see that your application is written in English as it may be reviewed by international experts with English as their working language.
Note or Other details
Type of employment: Fixed-term employment, 2 years
Extent: 100% Location: Department of Swedish, Multilinguality, language technology
First day of employment: 2022-08-01 or by agreement
Union representatives at the University of Gothenburg:https://www.gu.se/en/about-the-university/work-at-the-university-of-gothenburg/how-to-apply
Information for International Applicants
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The University of Gothenburg promotes equal opportunities, equality and diversity.
Salary is determined on an individual basis.
Applications will be destroyed or returned (upon request) two years after the decision of employment has become final. Applications from the employed and from those who appeal the decision will not be returned.
The University works actively to achieve a working environment with equal conditions, and values the qualities that diversity brings to its operations.
Salaries are set individually at the University.
In accordance with the National Archives of Sweden’s regulations, the University must archive application documents for two years after the appointment is filled. If you request that your documents are returned, they will be returned to you once the two years have passed. Otherwise, they will be destroyed.
In connection to this recruitment, we have already decided which recruitment channels we should use. We therefore decline further contact with vendors, recruitment and staffing companies.
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If you have questions regarding the position, please contact: Nina Tahmasebi, researcher associate professor. Email: [email protected]
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