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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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-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches. Neural solvers
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quantification. The specific project will be tailored to your expertise and interests; examples include: Efficient inference techniques for high-dimensional Bayesian inverse problems for image reconstruction and
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vulnerabilities and devise static and/or dynamic approaches to detect them in the code or prevent their execution at runtime. The focus will be on a fuzzing approach in a Java/Android environment. Keywords
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resources, the doctoral student will use various experimental platforms within LUMIA (LUMIA - Luleå Material Imaging and Analysis ) as well as national research infrastructures through MAX IV Laboratory
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restoration ecology (see https://www.slu.se/en/about-slu/organisation/departments/department-of-wildlife-fish-and-environmental-studies/ ). The department has many international employees and well-established
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and Data Analytics, Fraunhofer-Chalmers Centre E-mail: mats.jirstrand@fcc.chalmers.se Phone: +46 730 794303 Where to apply Website https://academicpositions.com/ad/chalmers-university-of-technology/2026
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imaging, histology, immunohistochemistry, protein analysis, and advanced sequencing and transcriptomic approaches. Experimental therapies administered topically will be evaluated, including their effects
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dynamic approaches to detect them in the code or prevent their execution at runtime. The focus will be on a fuzzing approach in a Java/Android environment. Keywords for this project: code analysis, static
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. You will also characterize the eye using in vivo imaging, histology, immunohistochemistry, protein analysis, and advanced sequencing and transcriptomic approaches. Experimental therapies administered