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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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., 2019; Pedersini et al., 2023). We combine ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a
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for interfacing and interrogating cell and organoid models • Develop a deep understanding of cell-material interactions using an array of characterisation techniques ( e.g. 2D and 3D tissue reconstruction
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project will likely use a combination of single particle cryoEM, cryoET, and X-ray crystallography, you should be an expert in at least one of those techniques and keen to learn the others. You also should
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for the conversion of small and low energy molecules into advanced chemicals. The researcher will build up a deep understanding of the synthesised thin films, measure the electrocatalytic performances of the thin
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qualifications include: Experience with radio interferometric observing, data processing, and imaging. Experience with modern machine learning / deep learning techniques and software packages. Experience with time
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. Strong programming skills. Familiarity with popular Deep Learning platforms such as PyTorch and TensorFlow. Preferred Qualifications: Expertise in vision transformer or large language model. Expertise in
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datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep learning architectures including generative models, particularly
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is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded research focuses on a
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum