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generally to develop new distributed edge services. The successful candidate is expected to evaluate metrics such as latency, throughput, availability, and resilience in different applications scenarios
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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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Post-doctorate position (M/F) : Exascale Port of a 3D Sparse PIC Simulation Code for Plasma Modeling
to exascale architectures an initial 3D simulation code developed as part of previous work [1]. This work will initially focus on scaling up (distributed memory), optimizing CPU algorithms (vectorization) and
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Description This project aims at developing plastic neural models that capture the insect navigational plasticity and resilience. 36 months (renewable +12 months) Simulation of navigating ant-agent models
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 2 months ago
this storage format. This is a crucial tool for modeling certain aircraft physics. The development of the h-matrix library is the fruit of a collaboration since 2010 between Airbus and Inria, it is in C++ and
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- ical instabilities. By leveraging CADNA, we will automatically identify instability sources and try to fix/bypass them by combining our expertise on NEMO algorithms with CADNA developers' knowledge
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consumption of Wi-Fi interfaces and to evaluate by simulation the dynamic adaptation algorithms developed within the FACTO project. - design of energy models for Wi-Fi interfaces - implementation and validation
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will develop
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, reinforcement learning, temporal logic, automata learning, etc. More specifically, we will explore reinforcement learning techniques to develop black-box testing algorithms for timed automata. We will explore