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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
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, creativity, rigor, ownership, and excitement to push research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, data management, and
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial
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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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. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
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, their achievements and productivity to the success of the whole institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Robotics offers a full-time
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science. A wide range of quantum theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to