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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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publishing in peer-reviewed scientific journals Your Profile Master’s degree in electrical engineering, physics, computer science, or a related field Strong academic background in embedded AI and in the
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solving security assessment and optimization problems at unprecedented speed and scale. While high-performance computing (HPC) has become the backbone of large-scale power system simulation, emerging
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collaboration with energy system modelers Your Profile Completed master's degree in a STEM field; a degree in computer science, data science, or an AI-related discipline is preferred Strong interest in
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Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new
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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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two companies. The project has partners from eight different EU countries. All 15 Ph.D. projects are within the overall theme of neuromorphic computing and analog signal processing, targeting
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; PyTorch; parallel and GPU computing Applicant Profile The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or
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qualifications master student or equivalent (2nd/3rd engineering school year) in computer science and/or machine learning; some knowledge of the Python programming language; ability to work in a team
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the specific projects above you seek to join, your profile should cover some of the skills listed below. Masters degree in electrical/electronic engineering, computer science, computer engineering