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About the project: Machine Learning to Unlock the Mysteries of Metallic Phase Transitions Supervisor: Dr Livia Pártay, University of Warwick Join a PhD project that goes beyond state-of-the-art to
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, the candidate will be deployed in the entire portfolio of application-oriented research activities within the InstrumentAI project, conducted in collaboration with industry. The position focuses
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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of this particular PhD fellowship is to develop innovative applications, tools and models for AI game-based learning. Further, in collaboration with a postdoc, the candidate should establish models, frameworks, and
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research capacity – in 2026, we are expanding with four postdocs who are expected to work closely with the ph.d. students hired in this call. As a PhD student, you will join a highly collaborative
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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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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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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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pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental
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student, you will join a highly collaborative, international, and ambitious research community, with access to state-of-the-art computational resources. The positions feature the possibility of going on one