92 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" positions at KINGS COLLEGE LONDON
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(EHR) data built by a multidisciplinary team of software developers, machine learning engineers, clinical researchers and health informaticians. The CogStack team is at the forefront of building
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://www.kcl.ac.uk/lsm/index.aspx Within FoLSM, the School of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application
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(EHR) data built by a multidisciplinary team of software developers, machine learning engineers, clinical researchers and health informaticians. The CogStack team is at the forefront of building
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disorder areas in 54 studies. The Software Engineer will be responsible for implementing and optimising infrastructure and deployment processes, and will also contribute to the development of key software
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disorder areas in 54 studies. The Software developer role will be pivotal in expanding the accessibility and utility of the RADAR-based platform, impacting the pace and quality of mobile health research
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multiple fields and audiences. This is a full time post (35 Hours per week), and you will be offered an a fixed term contract until 31 August 2028. About you Please note that these are development positions
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the university, and with our external partners, to design, develop and deliver a wide range of educational programmes. Our learners study with us from across the world, choosing when and where they study, working
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disorder areas in 54 studies. The Software developer role will be pivotal in expanding the accessibility and utility of the RADAR-based platform, impacting the pace and quality of mobile health research
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to improve Student Welfare and Engagement that is gaining traction across our University. We need enthusiastic developers to help us take a niche product into general mainstream usage. Responsibilities
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with