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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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work closely with fellow PhD students and postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and
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quantitative experts (mathematics, statistics, AI&ML, systems engineering). Supervise and support PhD students and postdocs involved in joint projects. Help establish new collaborations with industry and public
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, diagnosis, drug response and health monitoring. Research in precision medicine is expected to use existing strong assets in Sweden and abroad, such as molecular data (e.g. omics), imaging techniques
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optical communication - from long-haul transmission to datacenter interconnects - we tackle real-world challenges at every scale. We’re a collaborative research team, where PhD students and postdocs work
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(mathematics, statistics, AI&ML, systems engineering). Supervise and support PhD students and postdocs involved in joint projects. Help establish new collaborations with industry and public-sector stakeholders
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resonance imaging, and behavioral testing in a large sample of individuals experiencing intense grief at least twelve months after bereavement. In this way, the thesis will create a multimodal and multilevel
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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student will use various experimental platforms within LUMIA (LUMIA - Luleå Material Imaging and Analysis ) as well as national research infrastructures through MAX IV Laboratory, SciLifeLab, ARTEMI, and
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential