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particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine
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free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate collaborate with
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validate mechanistic and hybrid AI models of fungal growth, polymer degradation and bioreactor performance. apply machine learning and optimisation to guide experiments and improve fungal process
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experience with artificial intelligence or machine-learning tools and methods, particularly their application to mathematical research or software development. This may include AI-assisted programming