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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 11 days ago
of Adrien Taylor and Scott Pesme. The successful candidate will implement and compare different parallel and stochastic numerical optimization paradigms accurately study the selected algorithms, participate
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 11 days ago
contribute to the design, development, and experimental validation of novel graybox tunneling algorithms for multi-objective combinatorial optimization, with a particular focus on their parallelization and
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
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ASIC implementation. You will carry out system modelling, design novel parallel processor architectures at the RTL level (using Verilog/SystemVerilog or VHDL), and drive the hardware-software co-design
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Biological & Life Sciences Division – Postdoc Researcher: AI for Precision Medicine and Therapeutics
, machine learning, programming in C, C++, Python Scripting languages and MATLAB, using a parallel computing environment Bioinformatics, network biology, network medicine, network analytics, AI, ML, DL, LLMs
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quantify the impact of fast, tenuous shocks on ionized line emission. In parallel, high-resolution 3D MHD simulations of fast shocks interacting with dense interstellar clouds will be carried out with
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(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative
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the following areas is preferred, and an exceptionally strong candidate in a single area is also encouraged to apply. Relevant areas include: Parallel and distributed graph and or ML algorithms