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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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(https://www.hereon.de/index.php.en ) and Institute of Surface Science (https://www.hereon.de/institutes/surface_science/index.php.en ). Collaborators: Uppsala Universitet, Sweden and Quintus
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in chemistry and biology, approaches for extracting relevant information from foundation models, and/or methods for adaptive experimental design such as active learning or Bayesian optimization
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the virtual embryo; and Bayesian Inference to calibrate model parameters and identify developmental control points. You will also gain experience in the simulation-experiment loop, where computational
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circuit mechanism underlying higher cognitive functions such as multitasking, rule-based reasoning and Bayesian inference). In addition to the above areas, there is extensive expertise available in
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Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows, or Diffusion Models, is also a plus. The position includes a competitive salary, research