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About us At the Centre for Developmental Neurobiology (CDN), we investigate the mechanisms governing the formation of the brain during embryonic development and in early postnatal life. This is
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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mechanisms of anxiety after cancer’. The award is supporting a large-scale collaborative research programme between KCL, UCL, Stanford University, and the National Cancer Institute (NIH) through 2025-2030
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in neurodevelopment. This role will focus on data analysis and methods development to develop a better understanding of the mechanisms underpinning typical and altered neurodevelopment, brain structure
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About us At the Centre for Developmental Neurobiology (CDN), we investigate the mechanisms governing the formation of the brain during embryonic development and in early postnatal life. This is
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United Kingdom Application Deadline 7 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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, transcriptomics, biochemistry, and molecular biology. The successful applicant will be expected to work both independently and collaboratively, contributing to a broader understanding of the mechanisms underlying
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their relationship with treatment responses and disease activity. This is an excellent opportunity to contribute to a collaborative research program dedicated to improving our understanding of ALS and informing future
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene