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Field
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-node GPU training and inference pipelines for foundational models. You'll also develop tools for ingesting, transforming, and integrating large, heterogeneous microscopy image datasets—including writing
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and GPU-accelerated tools for circuit and system design optimization, addressing challenges in physical design, timing analysis, and large-scale hardware design automation. The researcher will
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simulation methods, GPU-accelerated computations, several programming languages, and presenting results to wide technical and non-technical audiences. Additionally, the candidate will also develop theory and
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variety of computational devices (e.g. CPUs and GPUs) while ensuring overall consistency and performance. - contribute to identify new CSE applications domains, such as condensed matter systems, quantum
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models on GPU infrastructure (SSH access) and distributed computing environments. Strong problem-solving, documentation, and communication skills across technical and non-technical contexts. Ability
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pipelines, and rigorously quantify reductions in energy per solve compared with optimized CPU/GPU and FPGA baselines. The project targets three real THz-NDE use cases: (i) sparse deconvolution of THz impulse
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: LightKrylov/LightROM libraries, Incompact3d solver, Pprime CPU/GPU infrastructure, IUSTI experimental database. 4- PRINCIPAL TASKS AND RESPONSIBILITIES ----------------------- a.Stability analyses: Perform
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research programme Access to secure clinical and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with
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develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU-accelerated signal processing pipelines, improving system
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct