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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
University. Project description This fellowship forms part of the Novo Nordisk Foundation-funded project “Deep Learning-Accelerated Crystallography Pipeline”, a collaboration between Durham University
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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experts in making better-informed decisions. You’ll gain expertise in 3D deep-learning, semantic technologies, explainable and uncertainty-aware AI, and human-centred evaluation, and test your work in
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have learned during the day. Using a gentle sound played during deep sleep, linked to a therapy session, we aim to help the brain hold on to the progress made in therapy. The student will use wearable
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning