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Field
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will design, implement, and validate novel algorithms, and benchmark them against state-of-the-art reconstruction pipelines. Strong programming skills (e.g., Python/C++ and GPU-based computing) and
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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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20 Feb 2026 Job Information Organisation/Company University of Jyväskylä Research Field Chemistry » Computational chemistry Chemistry » Other Computer science » Programming Computer science » Other
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-world scenarios. Strong programming skills in Python, PyTorch, and/ or JAX are required, along with the ability to reason about neural network behavior from first principles. The role also requires
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ensure models can be deployed in large data real-world scenarios. Strong programming skills in Python, PyTorch, and/ or JAX are required, along with the ability to reason about neural network behavior from
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. Training LLMs, large-scale deep learning systems, and/or large foundation models using GPU/TPU parallelization while setting up the environment/system network under various constraints, such as limited
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23 Jan 2026 Job Information Organisation/Company ETH Zürich Research Field Computer science » Computer architecture Computer science » Programming Computer science » Other Engineering » Biomedical
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Domain-specific accelerators, e.g., GPUs, tensor cores, dynamic programming instructions (DPX) Software accelerators, e.g., DPDK, eBPF System security and Software Bill of Materials (SBOM) AI/ML