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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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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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the first call lasts from the 1st of July to 31st of August 2025. Description of specific PhD projects: Machine Learning Interatomic Potentials for Chemical Reactions Hosting: Tallinn University of Technology
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment