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11 Mar 2026 Job Information Organisation/Company CNRS Department Institut de Chimie des Milieux et Matériaux de Poitiers Research Field Chemistry Chemistry » Computational chemistry Researcher
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Requisition Id 16217 Overview: The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) section of the Computational Sciences and Engineering Division (CSED) at Oak
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advanced compilation techniques for scientific and AI applications on heterogeneous GPU clusters. Research topics include scheduling, memory management, communication–computation overlap, and performance
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Duke University, Electrical and Computer Engineering Position ID: Duke-Electrical and Computer Engineering-POSTDOC_YIRAN [#31802] Position Title: Position Type: Postdoctoral Position Location
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hardware architects to establish how agentic AI and these languages co‑design with heterogeneous HPC systems (CPUs, GPUs, PIM, AI accelerators). Study performance and portability tradeoffs, leveraging
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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | about 2 months ago
Behavior (CASCB), providing access to shared infrastructure, high-speed data networks, and central technical services. Computational needs will be met through multiple layers of resources: local GPU-equipped
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on small test clusters. Test computational performance and resolve technical challenges on significantly larger models of selected quantum materials. Work on speeding up Krylov solvers on GPUs. Demonstrate
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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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computing environments, and GPU programming. Necessary skills include knowledge of data processing using software (e.g., Matlab, R, IDL) and/or statistical/mathematical programming languages (e.g., R, Matlab
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI