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of antibiotic resistance. You will build generative protein models to predict plausible future resistance mutations, use these models to guide high-throughput experimental screens of millions of enzyme variants
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stability prediction Collaborating closely with experimental researchers for iterative model refinement Publishing high-impact research at the intersection of AI and molecular design Supervising students
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- specific predictive models, the lack of explainability in AI-driven decision processes, and the difficulty of capturing long-term dependencies in time-series data. In this project, you will focus
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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recycled, if they are contaminated with fungal biomass and mycotoxins. Therefore, control and prevention strategies for fungal contamination and growth is essential. The aim of this particular part of CEBE
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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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to simulate full system performance. You will work closely with the project partners to determine design specifications and also after the prototype has been realized to compare model prediction with actual
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have extensive knowledge on processes governing cross-shore transport and can use experimental data to develop predictive models. Experiences within numerical modelling of coastal processes is considered
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network