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). Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments. Run and manage high‑throughput workloads on HPC or cloud infrastructure
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and generative protein design (e.g. AlphaFold, OpenFold, Boltz, Chai). Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments. Run
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. Proven ability to work both independently and collaboratively in multidisciplinary environments. Excellent English written and verbal communication skills. Preferred Qualifications Python proficiency and
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their results in the context of the clients. Analyses will be conducted with several software packages for statistical data analysis (possibly including R, SAS, Python, …). The new colleague may acquire new
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open-source software packages and set up computational pipelines. As a bio-informatics research lab, we publish software packages in Python and R for specific, cutting-edge analysis of large-scale
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, including hands-on implementation Strong understanding of machine learning models and their development Strong analytical, problem solver, and programming skills for Python and Matlab are preferred Experience
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statistical and mathematical modelling of infectious diseases. You have experience with scientific programming (e.g. in R, Python) If you have experience with the research topic, this is a plus. You have
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, optimization, and continuous learning in a rapidly evolving field.Desirable: experience with multi‑omics datasets, single‑cell or spatial technologies, or basic scripting skills (R/Python). Our offer Access
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development, optimization, and continuous learning in a rapidly evolving field. Desirable: experience with multi‑omics datasets, single‑cell or spatial technologies, or basic scripting skills (R/Python). Our
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in C/C++ or Python. Knowledge of Linux (ubuntu), ROS 1 and/or 2, Gazebo and/or pybullet, Docker, git, Latex are a plus. Knowledge of SLAM, pose estimation, UWB, IMUs, VIO, LIO are a plus. Hands on