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mouse models of cancer. The project will use molecular biology, protein biochemistry, and enzyme assays to further characterise this agent and understand its mechanism of action. You will manage your own
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through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs into active learning frameworks Explore automated dataset
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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and
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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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, biophysical assays, and murine models of pulmonary embolism. A second goal will consist of integrating experimental findings with in-silico models and microfluidic flow systems to explore the interplay between