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trials, longitudinal cohort building, multi-modal imaging, biobanks, epidemiology, neuropathology, wearables and biotech, experimental disease models including animal and cell models, and basic science
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work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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. The postdoctoral researcher will collaborate closely with an engineering team responsible for process integration and prototype development Expected start date and duration of employment This is a 2.5–year position
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. Nature Physics20, 970 (2024)). You will also work on expanding our coherent imaging methodology to look at dynamics and phase switching in materials at the nanoscale (Johnson et al. Nature Physics19, 215
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Zheng. Your competences You have academic qualifications at PhD level, for example within computational biology, bioinformatics, spatial omics, or related areas. Experience with computational imaging
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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an experience in technology-assisted monitoring or computational image analysis. Expected start date and duration of employment The position will start in June 2026, with exact starting date as agreed between
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you: To be successful in this role, we are looking for candidates to have the following skills & experience: Essential criteria PhD level qualified in biology, radiobiology, or radionuclide imaging
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hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project
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of archetypical mitigation pathways with their differential deployments of GGR and SRM, and 6) Policy options and governance. Qualification requirements Applicants for a postdoctoral position hold a PhD degree in