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research. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a background in Natural
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 30 days ago
, biomedical engineering, computer science, physics, or a related discipline. A strong background in fMRI data analysis (e.g., FSL, FreeSurfer, ANTS) and very good programming skills in Python and Bash on Linux
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mathematics. Strong programming skills in Python. Ideally, also proficiency in at least one major deep learning framework (e.g., PyTorch, JAX). An early track record of research (e.g., a high-quality Master's
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the analysis of large-scale omics data (e.g., proteomics, metabolomics, transcriptomics), including basic programming skills in R and/or Python Documented experience with exercise testing (e.g., testing
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related discipline; An interest in the impacts of land use; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of
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ecology. A strong quantitative mindset is essential, including good skills in data analysis using R, Python or similar tools. Experience with trophic ecology and Bayesian approaches would be an advantage
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
work. Some experience in modelling of composite materials. Some experience in programming (Python, Fortran, CUDA or similar). Some experience in fluid mechanics and biomechanics. Personal characteristics
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deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as PyTorch
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simulation and machine learning funded by the Klaus Tschira Foundation. Profile Solid grounding in statistical mechanics Experience with machine learning and/or molecular simulation; strong Python and PyTorch