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Develop and implementing multi-fidelity learning methods that combine experimental and computational data to enhance our enzyme design capabilities.
Design and deploy active learning, Bayesian optimisation, and evolutionary design strategies to optimise our AI-generated enzyme design process.
Create and refine multi-modal models, such as multi-head neural networks and multi-output Gaussian processes, to integrate diverse data sources effectively.
Develop learning models that can operate under various constraints (cost, accessibility, etc.) and handle uncertainty and noise in data.
What you will bring
A PhD in Machine Learning, Computer Science, Applied Mathematics, or a related field with a focus on multi-fidelity learning or similar approaches.
Extensive experience with state-of-the-art learning paradigms, including active learning, Bayesian optimisation, and evolutionary design.
Strong expertise in developing and implementing multi-modal or multi-fidelity models, such as multi-head neural networks and multi-output Gaussian processes.
Proficiency in Python and relevant machine learning libraries (e.g., PyTorch, TensorFlow, scikit-learn).
GCS is acting as an Employment Agency in relation to this vacancy.
