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, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising
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industrial contexts remains limited due to several structural limitations: · limited interpretability of model behavior; · weak guarantees regarding robustness and reliability
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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page to watch video, or click here to open video) About the position The position is part of the research project “Prediction of genetic values and adaptive potential in the wild (GPWILD)” (https
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format. The application should be registered via Umeå University’s e-recruitment system Varbi (https://umustipendie.varbi.com/en/what:job/jobID:950146/ ) and submitted by the deadline 17th of September
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receiving structured technical guidance. Availability to attend on-site meetings or work in person for part of the scheduled time. This position requires eligibility for a VA appointment to access VA research