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Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
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Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In this project, we will focus on increasing validity
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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with advanced statistical techniques (optimal Bayesian, Markov Chain-Monte Carlo, etc.) to solve the forward and inverse problems involved. Additional information about AGAGE, CS3, and MIT atmospheric chemistry
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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. The PDRA will quantify the differences in calculated and measured experimental conditions by adapting the Geodetic Bayesian Inversion Software ( https://doi.org/10.1029/2018GC007585) ). Working alongside our
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) is developing ML methods supporting the experimental HT workflows and, so far, focused on intelligent design of experiments based on Bayesian Optimisation. The team at Cambridge has its own high
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models and algorithms, particularly within Bayesian, generative, or probabilistic machine learning frameworks, together with deep knowledge of causal inference, prognostic modelling, and individualized