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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 1 hour ago
gravitational-wave detectors on the ground (LIGO, Virgo, KAGRA, Cosmic Explorer, Einstein Telescope) and in space (LISA), techniques for the acceleration of gravitational-wave inference, including machine
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Neurobiologie (ZMNH) Main tasks You will join the Institute of Medical Systems Biology and the bAIome Center for Biomedical AI (baiome.org) to complement our lively and enthusiastic team of machine learning and
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or Postdoctoral position (m/f/d) - Interpretable Machine Learning for Catalytic Reaction Network Discovery. A full-time PhD or Postdoctoral position is available in a collaborative Max Planck research
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, informatics, computational sciences); at least two years working experience in the computational analysis of imaging, omics, or clinical data; strong proficiency with machine learning and statistics; strong
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group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 20 days ago
-Neptunes and gas giants (3 years). Developing next-generation techniques for atmospheric characterization through retrievals with machine learning, including techniques such as simulation-based inference
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training