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optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised
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programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in astrophysics Machine learning or data analysis for physical sciences Strong programming
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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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programming skills and practical experience with leading machine learning frameworks and modern AI environments, including multi- GPU model training and large-scale inference on dozens to hundreds GPUs, are
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benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers ( HAW ) – UAW for purposes of collective bargaining and
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benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers ( HAW ) – UAW for purposes of collective bargaining and
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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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Jülich supercomputers (GPU/CPU), or being combined with a practical modeling project. Your Profile You are currently enrolled in a Masters degree and are planning your thesis You are highly motivated
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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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