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the Radiological Image Analysis research group, we specialize in advanced image analysis methods for research applications related to metabolic and cardiovascular disease as well as cancer. The group members have
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of Information Technology website . The project will be led by Professor Carolina Wählby , within the Image Analysis unit of the department’s Vi3 division, working alongside researchers developing numerical and
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Physics (with focus on machine learning and bone microscopy analysis, Soft Matter Lab) The Department of Physics at the University of Gothenburg is located in the center of Gothenburg, with approximately
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School of Electrical Engineering and Computer Science at KTH Job description We are looking for a recent graduate with a keen interest in implementation and adoption of image analysis algorithms
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of the leading units in the area in Sweden with particular strengths in nutritional and computational metabolomics, dietary biomarkers, micronutrient metal nutrition, nutritional immunology, marine food science
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genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis. This project aims to use Variational Inference (VI) methods, enhanced by AI
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collaborative project at the division of Biophysics, Department of Applied Physics, KTH. The goal of the project is to use microscale acoustofluidic technology for the formation, development and analysis of 3D
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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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to the delivery capability of lipid nanoparticles. Bulk techniques (e.g. small angle scattering) will be coupled with single particle analysis to improve our understanding of how disease impacts the performance
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://ngisweden.scilifelab.se/ ). Description of work You will contribute to the development and implementation of novel methods and technologies for genome, transcriptome and epigenome analysis, both in bulk and at single-cell