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About us The UCL Laboratory for Molecular Cell Biology (LMCB, http:/www.ucl.ac.uk/lmcb) is an internationally renowned, multidisciplinary, molecular and cell biology department, located in the heart
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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of the three direction, each focusing on distinct but complementary areas of biomedical AI and data science: • Position 1: Postdoctoral Researcher in Medical Image Analysis (Pathology or Radiology) Duties
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interdisciplinary teams, the design and execution of research studies and data analysis, with an overall goal to further the mission of St. Jude to advance research and cures for pediatric catastrophic diseases. St
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interdisciplinary teams, the design and execution of research studies and data analysis, with an overall goal to further the mission of St. Jude to advance research and cures for pediatric catastrophic diseases. St
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and are committed to fostering an inclusive and diverse research community. Terms & Application Initial funding: 2 years Application deadline: 15th March 2026 How to apply: Visit UCL Jobs and search
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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acquire additional skills and be involved in the analysis of single-cell RNA Sequencing, WES, and in situ transcriptomics data. Trainees will present their findings at regular lab meetings, departmental
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Annotation of datasets and downstream integrative data analysis (e.g. pathway mapping of images) Support with teaching and writing publications We seek a candidate with excellent practical skills in MALDI
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will provide an overview of your potential compensation and benefits package. For more information: www.dekoskylab.org Google Scholar: http://scholar.google.com/citations?user=z6TwVlwAAAAJ&hl=en Remote