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, to develop novel AI-based approaches to restore and denoise Transmission Electron Microscopy (TEM) images. This position is part of a cross-disciplinary research effort supported by the Wallenberg Initiative
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personalize interactions based on spatial and behavioral cues. Thus, the project requires technical skills in the development and programming of embodied AI agents. You will collaborate closely with
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model
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how digital product passports (DPP), and ontology-based platforms for collecting and interpreting such data, can facilitate data sharing among actors involved in a new circular flow of flat glass
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verifiability for AI systems, based at the Department of Computer and Information Science. These positions are funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Wallenberg AI
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cancer. The goal will be to find genetic prediction models to be able to predict which childhood cancer patients have a high or low risk of toxicity in childhood cancer. Preliminary the doctoral project
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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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European Marie Sklodowska-Curie Doctoral Network FADOS. The successful candidate will join a cohort of 17 doctoral students based at 16 research groups in Europe and the UK. About FADOS: FADOS, Fundamentals