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, in vitro models, and large collections of well-annotated clinical specimens. We employ state-of-the-art computational biology/bioinformatics approaches to dissect acute and adaptive responses to RAS
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availability of large-scale genotype data across Europe, along with detailed register and survey data from several countries, to map out how specific genetic factors covary with and interact with our
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records at the TRUST platform (https://trustplatform.sg/) . Large-scale proteomics data have also been generated for these individuals. We are inviting a motivated Research Fellow to join our PRECISE-SG100K
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data linked to multiple large surveys with longitudinal individual-level data, school-level aggregated data, and genetic data. The successful candidate should have prior knowledge of quasi-experimental
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 18 days ago
-the-counter and prescription drugs, including biological therapeutics and generic drugs. This effort covers more than just medicines. Research Project: The objective of this research is to apply large language
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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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successful candidate will work in the AI in Medicine Lab, applying machine learning to cutting-edge problems in cancer genomics, transcriptomics, spatial transcriptomics, and large-scale bioinformatics
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and arts-based approaches Participate in data collection and analysis related to project pilots and training activities Contribute to the evaluation of project outcomes and impact Take a major role in
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cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You can find more information about working at NTNU and the application
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. · First author of one or more scientific peer-reviewed publications. · Experience working with and jointly analyzing large spatial data sets. · Willingness and demonstrated ability to work collaboratively