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performing atomistic simulations with Density Functional Theory and Molecular Dynamics. Data analysis and coarse graining in order to provide parametrisations for upper scale models (Kinetic Monte Carlo and
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policy. Dr. Kang’s research laboratory is focused in personalized testing pathways, translation of diagnostic innovations, and cancer screening. We develop predictive models, simulation frameworks, and AI
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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reconciliations and state filings; Creates and maintains internal audit narratives that document and strengthen the control environment across the university; Provides input and simulates complex financial models
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Description We are recruiting a contract researcher to join a project involving the development of predictive models for simulating material forming processes, within the mechanical engineering department
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. The research will employ numerical simulation to optimise heat flow within thermal batteries and to design compact heat-exchanger structures that enhance thermal conductivity and temperature uniformity
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. Contract The contract model to be used will be in follow the FCT scholarship holder contract draft available at the following link: https://ucppt-my.sharepoint.com/:w:/g/personal/ccabral_ucp_pt
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datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs
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manager and maintain regular communication with all other relevant stakeholders. Where to apply Website https://utc.recruitee.com/o/chercheur-contractuel-fh-modeles-predictifs-securit… Requirements Research
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to provide high quality test data for the validation of design and simulation models as well as to verify new configurations. Besides research activities you will also contribute to the lectures and