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independently and collaboratively. Strong organizational and record-keeping skills. Ability to learn and apply new experimental methods. Excellent communication and problem-solving skills. Commitment
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, resulting in publications at top-tier international conferences and journals; Develop theory, implementations, and open-source software for hyperbolic deep learning; Collaborate with other PhD students and
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: · A vibrant and collaborative research environment, supported by experienced supervisors with expertise in wildlife ecology, conservation technology, applied statistics, and deep learning · Access
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learning, and Bayesian optimization approaches to guide experiment selection and support closed-loop decision-making in the SDL. Work closely with collaborators to align model development with measurement
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consortium. The group offers well-equipped laboratories, a highly collaborative international environment and membership of the Cluster of Excellence "Machine Learning: New Perspectives for Science", which is
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theoretic understanding of the fundamentals of Machine Learning. Ability to act independently as well as to collaborate effectively with members of a larger interdisciplinary team, take initiative, be result
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healthcare. They collaborate with partners from all over the world to improve health outcomes for all. Radboudumc unites patient care, research, education, and corporate learning, which allows us to approach
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technology, practice, and learning, with a particular focus on how Work-Integrated Learning can contribute to developing professional competence in digital crime prevention. The PhD student will collaborate
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Understanding plankton biodiversity and ecosystem change by applying machine learning – A CASE studentship Lead Supervisor (DoS): Professor Abigail McQuatters-Gollop Second Supervisor: Dr Clare
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Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners Responsibilities and