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computationally and developing scientific software. Experience in Python is highly recommended, additional knowledge of performance-oriented modeling frameworks, either based on Python (e.g., JAX, Pytorch) or other
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Python are essential Excellent teamwork, collaboration and communication skills Proficiency in written and spoken English A stimulating, challenging and multifaceted research environment Good integration
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(preferably Python), multiple years of programming experience as well as profound knowledge of professional computer-aided design and 3D modelling In addition, you have experience in CAD/CAM (preferably McNeel
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against experimental data. Proficiency in Python, together with good software engineering practices such as version control and testing, is expected. Experience with high-rate dynamics, cavitation
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, ICML, ICLR) Proven ability to conduct independent, high-quality research Proven ability to write well-documented code in Python Strong motivation to work in a collaborative team environment and to
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statistical genomics, computational biology, computational genomics, or animal genomics. Experience with a programming language (e.g., python, R) and basic working knowledge with high-performance computing
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Programming skills in one or more relevant languages (e.g. Python, R, Bash) Experience with the analysis of omics data (e.g. genomics, metagenomics, metatranscriptomics); experience with additional data types
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parallel and distributed systems, including performance tuning Programming and tooling such as C/C++, Python, CUDA, OpenMP, and Spack Linux-based systems, scripting, Slurm, and general systems engineering
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working with multimodal data fusion and high-resolution remotely sensed data is required. Proficiency in programming, particularly in Python, is essential. Knowledge of deep learning approaches and
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) Familiarity with automation software (e. g. Hamilton Venus), scripting, or workflow scheduling (e.g. HighRes Cellario) and experience with standard programming languages such as Python, C++, Matlab or R