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scales through component-based parallelism. The focus of the post-doc position is to design and implement this pipeline -- developing native reasoning for rich combinatorial constraints, creating tools
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
is designed for candidates who have completed their PhD within the last two years and have experience as postdocs or industry researchers. This position offers a 12-month term with potential
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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(ANR-25-CE54-1716) studies the Abavus family of glossaries, which constitute a lexicographical chain parallel to that formed by the Liber glossarum and its descendants. Despite its importance in
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are contributing to the High Energy cosmic-Radiation Detection (HERD) experiment, a next-generation detector for the Chinese Space Station. In parallel, we pursue R&D for future gamma-ray missions and play an active
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properties (including structural, defect, electronic, ion transport, surfaces) and will link up with parallel experimental activities. Applicants should have a PhD in chemistry, physics or materials science
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dimensioning the experimental set-up. This step will be jointly performed with the two senior researchers and 2 technicians in CORIA laboratory. In parallel with the development of the experiment, we will be
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
Website https://jobs.inria.fr/public/classic/en/offres/2025-09541 Requirements Skills/Qualifications Eligible candidates should have a PhD in computer science. Experience in formal methods or in
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results by 15 January 2026. Founding Source: Project „Dynamics of processes around compact stars” is funded by NCN (contract number: 2023/50/A/ST9/00527). Project website: https://ra.cft.edu.pl/ About us
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA