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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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of large and complex data sets, spanning brain imaging, genetics and clinical data. The goal is to reveal more of the genetic architecture underlying brain structure and function and to improve our
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of progression to secondary acute myelogenous leukaemia (sAML). You will take a lead on developing data analysis approaches to search for targetable genetic, epigenetic, or epitranscriptomic mechanisms
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the planning and implementation of student field practicums and in the analysis of the information generated from their capstone experiences. UF is the state’s oldest, largest, and most comprehensive land grant
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About us: We are seeking an enthusiastic postdoctoral researcher with a PhD in genetics, statistics, epidemiology, data science, or related quantitative field to work on a our Wellcome-funded
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Physics Division that hosts the Langhammer Group provides further relevant information. Who we are looking for We seek candidates with the following qualifications: A PhD degree in physics. An experimental
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driven individual with a PhD in data science, computer science, biomedical informatics, or a similar background with some experience working with large datasets. Prior experience with healthcare is not
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large-scale simulation data effectively. - Ability to conduct independent research while collaborating successfully in a multidisciplinary team, with a record of scholarly publications that reflects
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uncovering companion biomarkers for precision treatment using cutting-edge bioinformatics and proteomics approaches. You will analyze bulk and clonal protein expression data from large melanoma cohorts
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science, automation science, or a related field, and convincing expertise in robotic hardware. Experience with machine learning and large language models is highly desirable. Prior experience in a biological setting