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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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services can be found at: https://www.uu.se/forskning/snpseq and https://ngisweden.scilifelab.se/ We are proud to deliver high-quality data and are accredited by SWEDAC as a testing laboratory under the ISO
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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modeling, machine learning, and AI techniques applied to biomedical data is a plus. Clinical Proteomics: Experience with clinical trial data, real-world evidence (RWE), and biomarker-driven trial designs is
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of Medical Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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aspects of both. The first direction concerns the data-driven discovery of dynamical rules underlying developmental trajectories. The aim is to develop and analyze quantitative frameworks that learn
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at https://nbis.se. Duties We are seeking a candidate who wants to help enable life science research in Sweden that goes beyond what is achievable by individual researchers, a single university, or a single