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of an extension, subject to funding. You will apply and develop cutting-edge machine learning methods to integrate and analyse multi-omic data to identify disease phenotypes. A key aspect of the role is to bridge
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, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as well as proven competence in
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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, applied mathematics, or a closely related discipline, with previous experience in statistical and machine learning methods and knowledge in basic biology. Applicants close to completion of their PhDs will
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). The researcher should have a PhD/DPhil in robotics, computer vision, machine learning or a closely related field. You have an excellent academic track record in topics relevant to robot perception. A specific
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(postdoc) Reference no.: 6036 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate about
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shortly) a PhD in Immunology, or other relevant discipline in Biomedical Sciences, and a BSc (hons) in a relevant Biological Science. Extensive and up-to-date theoretical and practical knowledge in
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and experiences. You should demonstrate: Essential Criteria: Hold (or expect to hold shortly) a PhD in Immunology, or other relevant discipline in Biomedical Sciences, and a BSc (hons) in a relevant
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. This interdisciplinary position sits at the intersection of robotics, machine learning, and sustainable chemistry. You will join a vibrant research environment at the University of Liverpool, building upon our team's
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candidates with expertise in areas such as: Construction Informatics Digital Engineering BIM and Digital Twins Artificial Intelligence and Machine Learning Automation and Intelligent Systems Smart