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Field
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vehicles and achieving net-zero targets, but they degrade over time, reducing performance and safety. A key issue is gas generation, which causes internal pressure build-up and can lead to cell failure, but
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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stimulation (TUS), a novel noninvasive method for targeting deep brain structures University of Maryland, Maryland, United States Bas Ankoné (Rijksuniversiteit Groningen) A new political climate? Innovating
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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proficiency in oral and written English, creativity, thoroughness, and a structured approach to problem-solving Additional qualifications Experience with one or more of the following areas is meriting: Bayesian
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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, modify and extend. The project will explore a range of AI predictive and generative methods, such as large language and vision models (LLMs/VLMs), inverse procedural modelling, Bayesian optimisation, world
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, including reinforcement learning, hierarchical models, Bayesian inference etc. More details of key responsibilities of this role, in addition to the essential and desirable job criteria, are available in
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
-driven and physics-informed learning techniques Development of simulation-based optimization methodologies, including Bayesian optimization, derivative-free optimization, multi-objective optimization