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Field
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-driven to take a deep dive into the unknown. You’re extremely capable, using creativity and ingenuity to rise to new challenges. You’ve got an excellent M.Sc. degree in cancer genetics, molecular biology
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on your background, the work will 1) focus on the interaction between microwave design and measurement methods, looking deep into the technological capabilities of GaN, or 2) focus on new methods
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challenges (such as raw material constraints, hydrogen availability, and infrastructure deployment challenges), and analyze deep uncertainties. The research will guide sustainable transition strategies
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deep learning theory, Bayesian statistics, and generative modelling, this work will advance our understanding of both the capabilities and vulnerabilities of modern AI systems. This will have potential
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, identify challenges (such as raw material constraints, hydrogen availability, and infrastructure deployment challenges), and analyze deep uncertainties. The research will guide sustainable transition
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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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trends, and their future developments come with deep uncertainties. Infrastructure policies must account for these uncertain drivers and their dynamic interaction with changes in intended use. As part of
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: Building interpretable causal models to explain patterns (e.g., congestion dynamics), enabling transparency in high-stakes decision-making. We combine statistical data mining, deep learning, and domain