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Any vacancies, thesis projects, available internships etc. will be announced properly through the usual channels and on this website.
Note that I will not reply to unspecific requests or unsolicited applications for internships, PhD or PostDoc positions etc.

  Michael Biehl, University of Groningen

  Full Professor in Machine Learning, Theory and Applications

click cover Michael Biehl, book cover, The Shallow and the Deep

  University of Groningen, Bernoulli Institute for Mathematics,
  Computer Science and Artificial Intelligence,
  Computer Science Department, Intelligent Systems Group

  Nijenborgh 9, 9747 AG Groningen, NL      m dot biehl at rug dot nl
  Room 5161.0584   Tel +31 50 363 3997    meikelbiehl at gmail dot com

  Honorary Professor of Machine Learning, University of Birmingham
  Center for Systems Modelling and Quantitative Biomedicine

 
   +++ new +++ list of publications (pdf and bibtex, including links)

  Research:
  Machine Learning and Computational Intelligence
          Theory and algorithm development for neural networks
          Learning Vector Quantization and Relevance Learning
          Applications in life sciences, biomedical data, astroinformatics
  Statistical Physics and Scientific Computing
          Theory of neural networks, dynamics of machine learning processes
          Monte Carlo simulations of complex systems
          Disordered systems, non-equilibrium growth processes

  Teaching:
  Neural Networks and Computational Intelligence
  Modelling and Simulation
  Introduction to Machine Learning


+++ +++ breaking news +++ +++

May 2025: After 11 months with 4 rounds of insignificant revisions requested by 6 reviewers, our paper
Explainable machine learning for movement disorders - Classification of tremor and myoclonus
      with first author E. van den Brandhof, is finally available online (open access)
      in Computers in Biology and Medicine 192 B: 110180 (2025)

April 2025: More than 6000 downloads of
"The Shallow and the Deep" since its publication end of September 2023.

April 2025: Three contributions have been presented at the 33rd ESANN 2025 and published in the proceedings:
The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function
      by O. Citton, F. Richert, M. Biehl
Interpretable machine learning for the diagnosis of hyperkinetic movement disorders
      by E. van den Brandhof, J.W. Elting, I. Tuitert, M. van der Stouwe, J. Dalenberg, M. de Koning-Tijssen, M. Biehl
Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach
      by M. Kaden, A. Engelsberger, R. Schubert, S. Lövdal, E. van den Brandhof, M. Biehl, T. Villmann

Any vacancies, thesis projects, available internships, etc. will be announced properly through the usual channels and on this website.
Note that I will not reply to unspecific requests or unsolicited applications for internships, PhD or PostDoc positions etc.