31 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at SciLifeLab in Sweden
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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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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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Department of Clinical Microbiology The Dwibedi research group at the Department of Clinical Microbiology and the Laboratory for Molecular Infection Medicine Sweden (MIMS; https://www.mims.umu.se
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is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university
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University one of Sweden’s most exciting workplaces. Read more about our benefits and what it is like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/ The position may be subject to security
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A
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representatives at the University of Gothenburg can be found here: https://www.gu.se/om-universitetet/jobba-hos-oss/hjalp-for-sokande Application To apply for a position at the University of Gothenburg, you have to
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in multimodal imaging. Experience in machine learning is highly valued. You will support user-driven research projects and develop integrated data workflows spanning light microscopy (confocal, super
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data. Much focus is on large scale analysis based on machine learning, deep learning/AI, as well as handling and analyzing large 3D microscopy data. You will work with shorter and longer projects and
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data