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longer-lasting charging strategy for Li-ion cells using two complementary approaches. (1) By testing commercial cells under various controllable stress factors and integrating lifetime prediction models
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maintenance strategies represents a major industrial challenge at the intersection of system dynamics, control theory, and advanced signal processing. Current approaches exhibit complementary limitations: data
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, and/or computational modeling. This position integrates rigorous experimental characterization with multiscale simulation to understand failure mechanisms and improve safety at the cell, module, and
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of the contact line, which is still only partially understood and predicted. The present thesis proposes to develop an original experimental approach based on the simultaneous coupling of several optical
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, and generate high-quality datasets for predictive microbial modelling and risk assessment. Responsibilities include contributing to the design and execution of food challenge studies, integrating
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workflow that maps first-principles electronic-structure data onto predictive atomistic spin-Hamiltonians and device-scale dynamical models. The candidate will run high-throughput, relativistic DFT
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The research associate will focus on Vision-Language Model based situation awareness and decision-making
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predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results. Where to apply
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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand