327 data-"https:" "https:" "https:" "https:" "https:" "https:" "Eindhoven University of Technology (TU" research jobs in Sweden
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includes a combination of experimental work, data analysis, as well as interpretation and presentation of research results. The main part of the work for the advertised position involves studies of specific
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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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biology, analytical chemistry, evolution, and data science. The workplace offers access to state-of-the-art core facilities for advanced microscopy and cytometry, cell and molecular biology, extracellular
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Information Technology conducts broad research in cryptography, computer security, wireless and wired networks. The Security Group has around 20 members. The main research areas are cryptography, privacy, and
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candidate will have the opportunity to: Establish new deep-learning-based algorithms to reconstruct images in 3D and 4D Develop new image reconstruction algorithms for 3D and 4D X-ray data analysis, merging
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Therapy (MMGT) at the Biomedical Centre (BMC). MMGT houses labs with a variety of complementary expertise in stem cell biology and gene editing. More information on the lab can be found at leighnd.github.io
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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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total of approximately 1,400 employees and 17,700 students spread across two inspiring campus environments in Karlstad and Arvika. More information at: kau.se/en/work-with-us Description We are seeking a
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has aimed to develop a two-part research programme. The first part focuses on developing and applying methods for 3D digital modelling, ranging from the collection of digital data from archaeological
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with