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Your tasks Contribute to the projects in the group at the intersection of programming languages, software engineering, and security Collaborate with the other group members Extend the theoretical
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released models Make evaluations run quickly and at scale: parallel execution on Alps, efficient use of inference backends, caching, and result tracking Reduce mismatch between evaluations during
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system for thin, durable structural interventions. It can be deployed through conventional casting for standard applications and through 3D printing for complex or non-planar geometries. In parallel
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/aarch64 platforms Familiarity with HPC networking and communication stacks: Slingshot, libfabric, NCCL and its debugging Experience with parallel filesystems (e.g. Lustre) and storage performance tuning
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Eligibility criteria Good knowledge of particle physics and quantum chromodynamics. Knowledge of computer programming is a plus. Website for additional job details https://emploi.cnrs.fr/Offres/CDD
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Friedrich Miescher Institute for Biomedical Research | Basel, Kanton Basel-Stadt | Switzerland | about 1 month ago
( fmicompbio.github.io ) and offers training in programming, bioinformatics, and reproducible research. The facility is a member of the Swiss Institute of Bioinformatics and actively contributes to
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of modern deep-learning architectures and training strategies, and experience designing and rigorously evaluating new machine-learning methods. • Excellent Python programming skills and strong hands
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analysis and proficiency in one or more programming or scientific computing environments, such as Python, R, or MATLAB; A strong publication record appropriate to the candidate’s career stage; The ability to
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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Birmensdorf ZH, Kanton Zürich | Switzerland | 13 days ago
Climate Science at ETH Zurich . You hold a PhD degree in hydrology, environmental sciences, climate sciences, or a closely related field. We seek candidates with strong skills in programming (e.g. R or
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approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038