News and Events: Open Positions

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Postdoctoral researcher in Statistical Machine Translation

Publication date
12 March 2014
Level of education
Salary indication
~2,427 to ~3,831 gross per month, based on 38 hours per week
Closing date
1 June 2014
38 hours per week
Vacancy number

The Institute for Logic, Language and Computation (ILLC) is a research institute at the University of Amsterdam, in which researchers from the Faculty of Science and the Faculty of Humanities collaborate. Its central research area is the study of fundamental principles of encoding, transmission and comprehension of information. Research at ILLC is interdisciplinary, and aims at bringing together insights from various disciplines concerned with information and information processing, such as logic, mathematics, computer science, linguistics, cognitive science, artificial intelligence, musicology and philosophy. Research is organized in three research groups: Logic and Computation (LoCo), Logic and Language (LoLa) and Language and Computation (LaCo).

Within ILLC, the SLPL Lab, led by Dr Khalil. Sima'an, is seeking to attract three-year postdoctoral researchers in Statistical Machine Translation. In March 2014 the SLPL Lab. consists of 6 PhD candidates, 1 postdoctoral researcher and a programmer. The SLPL Lab still has 5 vacancies among which multiple postdoctoral positions on two projects: STW DatAptor, and NWO-VICI.

Project description

  • Conduct research in SMT on, e.g., domain adaptation, semantic factors in SMT, paraphrasing, (hierarchical) reordering models, MT evaluation or statistical learning in SMT;
  • present and publish research findings together with team members in proceedings and journals;
  • program and test the developed algorithms in prototype systems and make these usable for other team members;
  • assist in guiding PhD and MSc students working on topics in Computational Linguistics at large;
  • while this is primarily a research position, the successful candidate will be expected to also make a small contribution to teaching.


Prospective candidates should

  • hold or be about to obtain a PhD degree in statistical natural language processing or a related research area with experience in statistical learning and/or machine translation;
  • have a proven track record of excellence in research, as witnessed by a strong publication list in relevant first-tier conference proceedings or journals;
  • have a proven experience in and affinity with programming and empirical experimentation with text (parallel) corpora;
  • have strong passion for research, a drive to publish and the wish to learn new skills through working with or assisting in guiding PhD and MSc students;
  • have good communication skills in English, both oral and written.

Further information

For further information on this vacancy please contact:


The postdoctoral position will be awarded for a period of 36 months, with a 12-month probation period. The starting date is as soon as possible. Based on a full-time appointment (38 hours per week) the gross monthly salary will range from ~2,427 to ~3,831 (salary scale 10), depending on experience. The salary will be augmented with an 8% holiday allowance and an 8.3% annual bonus each December. The Collective Labour Agreement (CAO) of the Dutch Universities is applicable.

The successful applicant will work under the daily supervision of Dr. Khalil Sima~an.

Job application

Applications (single PDF file) should consists of

  1. a brief letter of motivation,
  2. a detailed curriculum vitae,
  3. a list of publications,
  4. the names and contact details of three academic referees and
  5. a link to a website with an online version of PhD thesis and relevant publications (e.g., your professional homepage -- do not include articles or thesis in your application).

Applications must be sent to. NB: be sure to mention the vacancy number in the subject line. Deadline for applications is 1 June 2014, but recruitment will continue until position is filled with a suitable candidate.

Please note that this newsitem has been archived, and may contain outdated information or links.