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. The next key step is identifying sensors for many new metabolite and drug targets. Our goal is to use a range of novel in-house experimental methods to discover hundreds of new sensors and in parallel
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tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other
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. Experience with mammalian cell culture, molecular cloning, flow cytometric analysis, multiple-omics analysis, bioinformatics, and animal experiments is preferred. Be highly motivated individuals who can work
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the process of measuring and analyzing high-throughput, multi-dimensional omics data integrating single cell transcriptomics and spatial proteomics of immune cells from CSF and blood as well as human brain
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clinical trials, correlative tissue biomarker analysis, genomics studies. Skills or ability to apply computational approaches and implementation of bioinformatics methods is desired. Developing independence
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Role: The laboratory consists of experimental biologists and computer scientists who focus on the study of transcriptional control of cell growth, using functional genomics, systems biology, and mouse
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We seek a talented and highly motivated computational postdoctoral fellow with a strong background in bioinformatics and passion in exploring the heterogeneity and evolution of tumor cells and the
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to learn. Interest in tumor metabolism and a strong desire to gain experience in bulk and spatial metabolomics. Solid bioinformatics skills, including familiarity with tools for analyzing omics datasets and
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models of human disease and clinical samples to assess diagnostic and prognostic value. Engage in collaborative research with faculty and trainees across biology, chemistry, and bioinformatics
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Company Information: A full-time postdoctoral fellow position is available in Professor Wenyi Wang's lab at the Department of Bioinformatics and Computational Biology, the University of Texas MD