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Holistic Insurance Enhancement using Learning Algorithms & Decentralization (S.H.I.E.L.D.)”. He/She will be required to: (a) develop machine learning models to predict optimal rehabilitation solutions
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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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on exercise health assessment and risk prediction for children and adolescent”. Qualifications Applicants should have: (a) an honours degree or an equivalent qualification in Biomedical Engineering, Sports
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- “Smart Holistic Insurance Enhancement using Learning Algorithms & Decentralization (S.H.I.E.L.D.)”. He/She will be required to: (a) develop machine learning models to predict optimal rehabilitation
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). The RAs will help us to improve an existing Python code base for creating 3D models of ancient artifacts from large datasets of 2D images. The main qualifications are prior experience working on large
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responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold, RoseTTAFold, ESMFold), molecular dynamics (MD) simulations, or protein-protein/antigen-antibody
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(e.g., scRNA-seq) to investigate cellular heterogeneity, host-pathogen interactions, and immune responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold
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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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, cooperative, and coding skills. Applicants are invited to contact Prof. Guohao Zhang at telephone number 3400 8488 or via email at [email protected] for further information. Conditions of Service
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, or other techniques relevant to the project) using software such as SPSS, R, Stata, or Python; (b) perform qualitative data analysis using NVivo (including coding), thematic analysis, and management