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infrastructure and surrounding terrain. The work can include physics-based modelling of e.g., various events and wave propagation through different media, signal processing, large-scale data analysis, algorithm
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mechanisms, biomarkers, and therapeutic targets in solid tumors through multi-omics data analysis of large pan-cancer datasets, including proteogenomic data generated across high-throughput profiling platforms
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subject area relevant to the post and with a significant quantitative component - Have or be about to obtain* a relevant PhD in cognate subjects. - Significant administrative data research experience and skills
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research
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28th October 2026 Languages English English English PhD Research Fellow in Learning-based Control of Autonomous Robot Manipulation Apply for this job See advertisement About the position A fixed
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of research and participant recruitment strategies Publish results of own research Supervise PhD students and research associates Capturing data from participants and their families Contribute to the Department
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This position is salaried and benefits eligible. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu
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these people live. Basic Qualifications The postdoctoral fellow position requires a PhD in environmental health, environmental engineering, environmental science, microbiology, or a related scientific field by
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scientists. The NOMIS-ISTA Fellowship Program provides postdocs with the freedom to work on big unanswered questions and high-risk research at the intersection of two or more scientific disciplines. ISTA
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at risk. Most patients therefore present with advanced disease which responds poorly to standard treatment. Working alongside a UK clinical trial, we have generated large immune profiling data sets from