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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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researcher Are you excited to help 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
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coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics
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) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered an advantage but is not required Motivation to conduct excellent scientific research and publish in leading international journals
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infrastructure 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
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, 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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health and welfare benefits. A supportive team environment that promotes collaboration and knowledge sharing. Access to world-class computational infrastructure, GPU-based computing environments, and
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with transistor-level circuit design and simulation What We Offer We provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard
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experiment or project. ABOUT THE LAB We encourage applicants to read more about our scientific goals on our lab website (https://hscrb.harvard.edu/labs/arlotta-lab/). In addition, the Arlotta lab is
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-level circuit design and simulation What We Offer We provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard circuit simulation tools