What you will do

• 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.

• 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.