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Field
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understanding of AWS services (EC2, S3) or similar cloud platforms for compute and storage. Version Control & CI/CD: Git, automated testing, deployment workflows. Experience with Linux systems, HPC , and
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coding environments, cloning repositories, command-line/terminal execution, Linux OS, building code from source Knowledge and education covering the major systems in human physiology and pathophysiology
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applications in Linux environments, including configuration of software libraries, package dependencies, and runtime environments for open source and for ESRI geospatial, scientific computing, or
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quantum field theory Experience with programming languages, in particular C/C++/Python Experience with Linux and high-performance computing Willingness to contribute with talks to international
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models for biomedical applications. Strong programming experience in Python and/or R. Experience with Git/GitHub and collaborative software development. Experience working in Linux and high-performance
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doctoral programme in question, the following are also required: Proven experience working in developing Linux AND Python-based computational workflows for advanced scientific data analysis and
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in Linux/Unix-based high-performance computing ( HPC ) environments and job schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS , GCP , Terra) is a plus
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Max Planck Institute for Biological Cybernetics, Tübingen | Tübingen, Baden-Württemberg | Germany | about 2 months ago
Linux are required. Prior experience in MRI data acquisition is desirable. Experience with ultra-high-field MRI (e.g., 7T) or layer fMRI is an advantage but not required. Experience with PsychoPy (task
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d'assurer l'intégrité et la sécurité de la nation. Profil du candidat Physique atomique des plasmas, mécanique quantique, physique statistique, programmation. Linux, Python. Bac+4/+5 Localisation du
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AI and machine learning, image analysis, Linux-based high-performance computing and scalable data pipeline development. They will be motivated to build reproducible, open-source computational tools and