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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in
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, and stimulating environment. We value communication and collaboration and a workplace that promotes learning and development for all employees. We are also committed to building a safe and positive
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collaboration we combine theory, observation and technical innovation to advance astrophysical knowledge and develop the next generation of research methods. We also work in close collaboration with Onsala Space
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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. The position shall include the opportunity for three weeks of training in higher education teaching and learning, as well as opportunities for supporting Prof. Kamerlin in the co-supervision of degree projects
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of the algebraic geometry group at KTH are Mats Boij, Sandra Di Rocco, Kathlén Kohn, Georg Oberdieck, David Rydh and Roy Skjelnes. There is close collaboration with the algebraic topology group at KTH and the
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successful and well-funded research group, and will be surrounded by senior and junior researchers. Our research group collaborates with other groups at the department and prominent research groups around the
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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regular project meetings and collaborate closely with other members of the research group. Publish scientific articles, both independently and in collaboration with others. Teach up to 20% of your working