The Department of Psychology at Stockholm University invites
applications for a postdoctoral fellow in computational linguistics /
cognitive science. The position involves a full-time employment for a
maximum of two years, starting on November 1 2022, or as per agreement.
The applicant will work in a research project that investigates
crosslinguistic differences and similarities of smell vocabularies. In
the project, smell vocabularies of diverse languages are mapped partly
with the help of data-driven machine learning methods based on natural
texts (i.e. text corpora), and partly based on more traditional
lexical-typological, linguistic methods based on native speaker

Main responsibilities
– analysis of text corpora using NLP methods, word embedding
modeling, machine learning and similar methods
– quantitative data analysis and visualisation
– writing of scientific articles
– possibly also experimental data collection


Stockholm University


Required qualifications
– PhD in linguistics, cognitive science or similar, completed no more
than three years before the application closing date (with some exceptions).
– high-level skills in Python programming
– documented experience in working with text corpora and various NLP
methods and word space modeling (such as Word2Vec, BERT, GPT2)
– documented experience of independent scientific work resulting in one
or several publications in scientific journals or conference proceedings

Desired qualifications
– documented experience of machine learning and other statistical
methods such as regression analysis, PCA, hierarchical clustering, or
neural networks
– documented experience of text data processing and pre-processing,
compilation of data from different sources, data visualisation
– documented experience of experimental or survey data collection
– good collaboration skills, be innovative, solution-oriented and able
to work independently
– basic knowledge of linguistics and cognitive psychology

Educational level:

Ph. D.

How to apply:

Please mention NLP People as a source when applying


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