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provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard circuit simulation tools (Cadence, Synopsys, etc.) on our own computing cluster
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find and assess the most promising habitable exoplanets around solar-like magnetically active stars? Join us to build next‑generation GPU‑accelerated models of stellar dynamos and connect them
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, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI, and the Aalto
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for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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, POPL), verification (e.g. CAV) or top venues in closely related fields. Experience in at least one of the following (ideally more than one): formal verification tools (especially proof assistants), GPU
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(especially proof assistants), GPU programming, low-level systems engineering in languages such as C/C++/Rust, systems for ML. Experience in writing academic papers. Please see job description for a full list
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team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
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large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use