5 image-processing-and-machine-learning Fellowship positions at University of Nottingham
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environment. In this role, you will lead the computational strand of the project, applying molecular simulations, data analysis, and machine learning to uncover how molecular structure, charge, and surface
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expertise in a supportive and innovative environment. In this role, you will lead the computational strand of the project, applying molecular simulations, data analysis, and machine learning to uncover how
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and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre at the Nottingham Breast
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unique combined system using an optimised AF scanning procedure that integrates Raman measurements to analyse lymph node biopsies within 10 minutes and machine learning algorithms to deliver quantitative
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised