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. Machine-learning and deep-learning techniques will be developed and compared for cancer-risk prediction, classification and prognostic modelling. Explainable and Multimodal AI Explainable AI (XAI) methods
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. The position combines Empa UESL’s expertise in developing and accessing energy system models with the methodological expertise of the IMOS Laboratory in machine learning and foundation models. Postdoctoral
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and
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. Major duties include: Calculate interaction energies from density-function theory Train machine learning models/potentials Conduct atomistic simulations to explore adsorption/diffusion Analyze
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at developing methodological contributions at the intersection of computer vision, multimodal learning, predictive world models, embodied AI, and human-robot interaction. The candidate will work towards models
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. This study aims to construct a transparent, non-invasive predictive framework combining machine learning and explainable AI (XAI) to differentiate malignant from benign pelvic masses, stratify patient risk
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, based on simulation models in Aspen, COMSOL, etc. c. Control theory & applications — including nonlinear systems, MPC, reinforcement learning, stability analysis, etc.
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Responsible ML Explainable Machine Learning techniques will be explored to identify important clinical predictors and improve the transparency of model predictions. The research will consider model robustness
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insights into actionable strategies for pediatric care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data
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of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training