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provides a vibrant international research environment, including a large community of researchers working on supernovae. The centre hosts a broad range of activities, seminars, and collaborations
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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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these plants. The post-doc is expected to build upon existing in-house tools and, where applicable, enhance them by means of AI (machine learning) and data-driven methods. These models are aimed to support
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meteorology, especially cloud microphysics, including the initiation and growth of ice particles; machine learning (e.g. neural networks). Consideration will also be given to good collaborative skills and drive
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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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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green transition is located at the Division of Human Rights Studies, Department of History at Lund University. The position is linked to ongoing research projects on digital exclusion – in collaboration
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digital advancements are transforming scientific communication, writing practices, and learning in STEM. The division offers ample opportunities for collaboration and networking, both within Chalmers and
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to allergen-specific immunotherapy. The research group conducts experimental and translational research in close collaboration with specialists at Skåne University Hospital. The team collects samples
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learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. The postdoctoral position is proposed around the following project