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you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase
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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students
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, and you are expected to develop showcases for this new platform, and develop ideas to implement in a business. For this you will learn and exchange ideas within the Biotech Booster community
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UC Berkeley after obtaining her PhD from the University of Amsterdam for which she did research at MIT and Sigma Computing. Her general research interest is on the intersection of machine learning
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of Amsterdam. Interested in developing fundamental machine learning techniques for tabular data to democratize insights from high-value structured data? Then this fully-funded 4-year PhD position starting Fall
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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University. Requirements A master’s degree in (applied) mathematics (or related), with a strong background in computational methods, preferably also using computational frameworks for machine learning in
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networks, or network science, and relevant background knowledge n methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples
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and differentiable programming applications (e.g., experimental design, machine learning for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: 1
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records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records