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autonomy over the portfolio supported, this position leads all aspects of proposal development, compliance assurance, and institutional coordination for submissions to a wide range of federal, foundation
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
determination by developing mathematical methods and integrating machine learning into crystallographic workflows. The successful candidate will develop theoretical and computational approaches to improve
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, instruments and facilities, and NRC Herzberg will provide them with the experience, support and professional development to help them grow into this role. Active areas of research at NRC Herzberg include Solar
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will support the end-to-end delivery of the Mastercard Foundation Scholars Program at the University of Oxford, with a particular focus on strengthening leadership, onboarding and development programmes
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candidates within PREFERENCE receive interdisciplinary training and develop a unique combination of scientific, technical and transferable skills. The consortium aims to develop new LAFOV PET/CT methodologies
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combining interdisciplinary scientific training, research exchange, and career development Access to state-of-the-art research environments across participating institutions Funding for annual participation
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. Develop/deliver complex campus-wide training, develop and implement internal reporting and analysis tools. Develop policies/internal controls. Manage special projects. Minimum Qualifications Bachelor's
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degree in Computer Science, Artificial Intelligence, Software Engineering, Robotics, or a related field Strong experience in software development and excellent programming skills, preferably in Python
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initiatives for the organisation, working closely with senior leaders and other stakeholders to ensure the best outcomes for our people. You will drive the development, review and implementation of University
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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally