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Field
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) research experience in one or more of the areas of intelligent fault diagnosis and condition monitoring, machine learning and deep learning; and (c) demonstrated ability to undertake high quality academic
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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning, Assessing
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in
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geological modelling and/or geophysical imaging Familiarity with site investigation, borehole logging, and geophysical approaches Experience with machine learning or deep learning is preferred Good written and
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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with deep insight and interest in investigating the interaction between collaborative learning processes in specific knowledge domains in educational sciences and AI tools and infrastructures. Different
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mentorship. Develop, implement, train, and validate machine learning and deep learning models for AI-driven prediction of cell physiology, metabolism, and functional behavior, with input from the research team