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- “Distribution mechanisms and risk prediction of fungal pathogens and antifungal resistance in urban waters”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no
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knowledge; (d) develop reliability-aware diagnostic methods involving uncertainty quantification, confidence calibration, conformal prediction, out-of-distribution detection, and unknown fault recognition
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encompassing data-driven electrolyte screening, molecular dynamics simulations, and experimental validation to bridge the gap between AI predictions and practical battery performance. The optimized electrolyte
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, and experimental validation to bridge the gap between AI predictions and practical battery performance. The optimized electrolyte formulations are expected to satisfy the strict requirements of high
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learning approaches; and (b) explore issues including imbalanced data, scalability, transfer, self‐supervision, etc., in prediction, inference, and planning with applications in medical images
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techniques such as thematic analysis and interview coding; (g) demonstrate competence in quantitative, qualitative and computational methods, such as inferential statistics and causal inference using