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and experience: Qualifications PhD in Computer Science, AI, Machine Learning or related field Experience A strong research track record relative to opportunity, including the ability to produce and
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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. Position You will work actively on the preparation and defence of a PhD thesis focusing on machine learning-based forecasting of renewable energy production, with a particular focus on wind energy. The
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for showcasing the improved mapping and monitoring of forest traits and uncertainties. You will be mainly in charge of: Develop improved hybrid model inversion methods with a focus on machine learning and deep
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of the Federal Armed Forces Hamburg (HSU/UniBw H), Faculty of Electrical Engineering, Professorship of Electrical Energy Systems (Prof. Dr.-Ing. habil. Schulz), a position is available as soon as possible for a
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Electrical and Computer Engineering. INESC-ID’s research impact is focused on four Thematic Lines Energy transition Life and health technology Security and privacy. Societal digital transformation. INESC-ID
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on the problem of making distributed machine learning robust to network outages and computational bottlenecks. The work is part of the Norwegian national AI centre SURE-AI, and the PhD student will
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engineering Engineering » Computer engineering Engineering » Control engineering Engineering » Materials engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a