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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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numerical simulations with optimization algorithms and software tools. · Carry out experimental validation of simulation results Is Your profile described below? Are you our future colleague? Apply now
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to contribute to other activities and tasks within the ENER-G project. The main outcome(s) of the PhD will be novel optimisation models and solution algorithms that will substantially advance the state-of-the-art
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