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partners. You will have access to Utrecht University GPU/HPC infrastructure and large, curated microbiome and microbial genome datasets. Additionally, you will collaborate closely with NOAH partners at
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-position) sufficient to ensure safe contact behaviour on torque-controlled platforms. Strong programming skills in Python and standard deep learning frameworks; fluency with at least one major robot
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. The project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck : https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons
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environment with a wide network of collaborators, generous support for conference travel, and access to national HPC resources (NAISS) and local GPU infrastructure. This Postdoc position is part of the
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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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intelligence, image understanding, or related areas Proficiency in Python programming; solid understanding of computer science fundamentals Experience with version control (GitHub) and AI coding assistants (e.g
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with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows. Demonstrated technical
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, or a closely related discipline. Demonstrated experience in scientific programming, scientific software development, or data-intensive computing. Experience with Python and at least one high
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jet physics, heavy-ion collisions, and detector performance studies. Experience in scientific machine learning, deep learning, foundation models, or multimodal AI. Experience with GPU programming
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and graduate research assistants, and presenting at design reviews and project milestones. Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high-performance GPU