1,926 computer-programmer-"https:"-"Brookhaven-Lab"-"https:"-"https:" Postdoctoral positions
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research students and performs literature reviews. Adheres to University and unit-level policies and procedures and safeguards University assets. Department Specific Functions Program Objectives: Provide
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) using a novel cell culture model. The postdoc will work on a comprehensive translational research program combining advanced human iPSC-derived co-culture models, spatial transcriptomics and proteomics
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significant scope for scientific creativity. You will have opportunities to publish in high‑impact journals, develop new modelling methodologies, work with high‑performance computing resources, and contribute
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for future career development in both academia and tech. Profile Required: PhD in ML, computational neuroscience, physics, engineering, or related field Strong experience in machine learning (PyTorch
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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supervisors, aligning with your expertise and the needs of the program. Core tasks include study design, data acquisition, analysis, and interpretation of imaging data. Work responsibilities An appointment as a
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Participate in teaching and supervision activities, in line with the candidate’s profile and interests The research activities will be hosted by the Parallel Computing and Optimisation Group (PCOG) at SnT
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and impactful research into intrinsic motivation, with a particular focus on building computational theories that predict human behaviour in the absence of extrinsic rewards, and testing them using
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platform for launching an independent academic career focused on AI and the future of scientific practice. Responsibilities Build and maintain computational data pipelines to collect and structure evidence