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Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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independently. Merits: Education or training in computer vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics
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representations Analysis of structure–function relationships between morphology and movement Modelling genome–phenotype relationships using machine learning and genomic language models The project offers a unique
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intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare. As an Industrial PhD
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical
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· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization