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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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on machine learning, and developing and applying simulation methods and models for equilibrium and nonequilibrium molecular dynamics simulations. You will model meta-lactamases enzymes involved in resistance
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methods based on machine learning, and developing and applying simulation methods and models for equilibrium and nonequilibrium molecular dynamics simulations. You will model meta-lactamases enzymes
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria
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specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis
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Educational Technology in the School of Computer and Mathematical Sciences. The successful candidate will be a researcher in the use of technology to support cognitive and meta-cognitive skills of students