Openings at


Applications of experimental algorithmic information theory.

Project description

Algorithmic information theory offers a general and universal way to discover structural relationships in data. This project would explore applications in artificial intelligence and bioinformatics that can benefit from recently developed classical and quantum algorithms for estimating algorithmic metrics. The domain exploration would include, but not limited to, explainable neural networks, causal invariance, gene regulatory networks, EEG data, cellular automata, and quantum variational circuits. A suitable use case needs to be chosen in the initial survey and a proof-of-concept would be implemented. The research conducted is expected to lead to a journal publication and a software package.


We seek a candidate with experience of programming in Python with a strong background in theoretical computer science. The candidate should have good scientific writing and presentation skills. Familiarity with the Wolfram language and TensorFlow is a plus. For M.Sc. thesis, the candidate must be enrolled in a masters program.


Remote working. Supervision and brainstorming meetings are generally in CET working hours.

Position role

M.Sc. thesis or Internship for a 6-12 months duration. Possibility of Ph.D. position and job offer in based on performance and funding.

Application procedure and deadline

Please send a CV and a motivation letter to or by July 15, 2021.