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aims to transform cancer research and precision oncology by developing a new generation of transparent, interpretable and trustworthy AI technologies that integrate machine learning, mechanistic
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to transform cancer research and precision oncology by developing a new generation of transparent, interpretable and trustworthy AI technologies that integrate machine learning, mechanistic modelling
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unrivalled online research platform. The three resources involved are; the PSIPRED workbench, an online suite of machine learning based tools for analysing protein structure, The CATH-Gene3D structural
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in the first instance. About you The successful candidate will be expected to conduct research of the highest international standard, working collaboratively with researchers across machine learning
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expansion, and machine learning. This work will have strong links to the experimental synthesis, diffraction and electrochemical work performed in the overall project. Previous experience in atomistic
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relevant standards and regulations (e.g. ISO 13485, ISO 14971, IEC 60601, UK MDR) is desirable, as well as experience of applying signal processing or machine learning methods to physiological or