26 machine-learning-phd-in-netherland Postdoctoral positions at University of London in Uk
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computational analyses of epigenomic/transcriptomic data and machine learning. Experience in single-cell omics data is desirable. The post holder will be responsible to develop pipelines for the analysis
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research into planet formation/protoplanetary discs or the ISM/star formation and may also have some experience in statistical methods and/or machine learning. Dr Winter and QMUL are committed to improving
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-edge machine learning techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
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also have or be close to completing a PhD in any of the following areas as well as the will and commitment to learn relevant topics from the other areas: Statistical and machine learning, mathematical
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the areas: AI, deep neural networks, machine learning, applied topology, probability, statistics, signal processing. About the School The School has an exceptionally strong research presence across
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interest to identify cancer drivers from genomic data using machine learning (Mourikis Nature Comms 2019, Nulsen Genome Medicine 2021), study their interplay the immune microenvironment (Misetic Genome
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infection experimental assays and the role will provide opportunities for career growth About You Applicants should have a PhD in a relevant field (or be close to completion) and be able to work independently
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-luminosity LHC. You will provide support to our PhD students and contribute to the broader activities of the group and the school. About You You will have completed or be about to complete a PhD or research
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, Spain and Norway. The project runs until early 2028 and investigates the potential role of performance-based arts in understanding how coastal communities learn about and respond to ecological crises
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal