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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 3 months ago
, diffusion, GAN) Good understanding of the mathematical foundations of machine learning Mastering python and related AI software technologies Proficiency in English Experience in parallel training of large DL
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Python and common deep learning frameworks (PyTorch) • Excellent communication skills in English (German is a plus) • Independent, structured working style and experience in supervising students Preferred
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biology approaches and early-adoption of cutting-edge technologies Operating with Linux and high-performance clusters (HPC) R/Python and Snakemake or Nextflow (or comparable platforms) OUR REFERENCES
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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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collaborative research. Where: Leipzig Start: May 2026 Details: https://uni-leipzig.b-ite.careers/jobposting/a9bfd0f27d1f4ddc2404831df4… Where to apply E-mail dekan@physes.uni-leipzig.de Requirements Research
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programming skills in Python and experience in developing and documenting reproducible research code (e.g. using PyTorch or comparable frameworks). Practical experience with AI-assisted development and coding
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biophysics, biology, biochemistry, or other relevant discipline Excellent written and spoken English skills High degree of independence and commitment Good knowledge of python and C++ Experience in relevant
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programming languages such as Python is advantageous. Practical laboratory skills and an enthusiasm for experimental work are highly valued. Participation in teaching activities (e.g., supervising practical
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(e.g. ALE, IBM, phase-field). The details of the position can be accessed through https://tu-freiberg.de/media/20936/download The application deadline is May 4th, with the start date around August 1st
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kinetic mechanisms and key elementary reactions involved. Addressing this shortcoming is the goal of this project. Please visit the DFG research unit description for more information on the topic https