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Field
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health using state-of-the-art imaging, sensors computational modelling and data science. The successful candidate will play a leading role in developing and implementing computational pipelines for MRI
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prototype and demonstrate its performance using real battery waste streams. Based within the School of Chemistry and Chemical Engineering and the School of Biosciences at the University of Surrey, you will
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research. Key Responsibilities: Develop, implement, and optimize machine learning/deep learning models for digital pathology image analysis Analyze large-scale histopathology, omics, and clinical datasets
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for Studying Congenital Kidney Diseases". The role will focus on human pluripotent stem cell culture, kidney organoid differentiation, genome engineering, disease modelling, molecular and imaging analyses, as
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neuroimaging and psychopharmacology. Our multidisciplinary teams combine expertise in neuroscience, psychiatry, imaging, and pharmacology to explore how drugs and psychiatric conditions influence brain function
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www.daslabpombe.com. For this research, we will use yeast genetics, live-cell imaging, protein dynamics, in vitro reconstitution, quantitative microscopy, and mathematical modeling approaches. Our lab is well equipped
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materials, and the use of this platform for realizing X-ray phase-contrast imaging on a table-top scale. Key Responsibilities: Develop theoretical and numerical frameworks for free-electron-driven X-ray
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-protein interactions using biochemical and biophysical approaches. ● Conjugation of aptamers to imaging or therapeutic payloads. ● Collaboration with protein biochemists, molecular biologists
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, combining behavioural, neurogenetic and in vivo imaging approaches, with the opportunity to train in specialist imaging techniques alongside an international collaborating laboratory. You will contribute your
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and develop advanced fluorescence and imaging approaches, including FRET-based assays, fluorescence lifetime imaging (FLIM), live-cell microscopy, site-specific bio-orthogonal protein labelling by