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for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
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embedded FPGA-based intelligence near seismic sensor arrays, you will explore methods to maximize prediction performance while minimizing data transmission and latency. Your key tasks include: Evaluating
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for interdisciplinary research at the interface of energy systems, high-performance computing, and quantum computing Strong background in mathematics, optimization, numerical methods, or scientific
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Computing Devices at CEA-Leti in Grenoble, you will contribute to the development of Bayesian learning methods suited to resistive memories (ReRAM). You will build on the team’s previous work, which
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two companies. The project has partners from eight different EU countries. All 15 Ph.D. projects are within the overall theme of neuromorphic computing and analog signal processing, targeting
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feeds Scaling analog in-memory computing towards large generative AI models H/F 192 / 778 vacancy Vacancy details Position description Category Micro and nano technologies Contract Internship Job title
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exactly solvable models) alongside large-scale numerical simulations on high-performance computing facilities Characterize the transitions that govern quantum information protection, from encoding and
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environment Of great advantage are: Experiences in numerical modelling Basic knowledge of data assimilation Experiences on high performance computing (HPC) Knowledge of FORTRAN programming language Please
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different types of experimental data to establish reliable methods for identifying physically meaningful model parameters and assessing model accuracy under dynamic operating conditions. In a later stage
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, scientific computing, and emerging AI. The project tasks will include resource-aware training methods and hardware implementation, including the design, implementation, and verification of KAN accelerator