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Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
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footprints and enhanced functionality. As a PhD researcher, you will: develop and validate high-throughput experimental methods to assess food ingredient functional properties; apply machine learning
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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
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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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Vacancies PhD ESG-Based Decision-Making for Customer Portfolio Management Key takeaways Insurance companies play a key role in financial markets—not by generating direct monetary value, but by
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
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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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collaborative mindset and bring the following qualifications: - a PhD degree in mathematics, with a focus on algebraic geometry; - experience with at least some of the following: Fourier—Mukai transforms