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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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with a strong background in mathematics, computer science, or machine learning. The work has a strong focus on developing new objectives or new architectures for medical deep learning and on new ways
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 3 months ago
and advance statistical, image analysis, and machine learning approaches Develop interpretable models that reveal biologically testable relationships and generate new scientific hypotheses Independently
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
analysis, image analysis, statistical, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a
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CFD simulations, led to a shift from performance towards efficiency, defined more precisely as minimisation of computer resources required for a given simulation. Concomitantly, the objective
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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sustainable operation of future energy networks by combining our knowledge of energy systems with cutting-edge developments in machine learning, generative AI, and digital infrastructures. Your Job
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing