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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, and
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including but not limited to microbial ecology, biochemistry, genomics, biostatistics, molecular biology, microbiology, evolutionary biology. Familiarity with metagenomics data analysis, microbial
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decisions focusing mainly on interpretable machine learning and its applications. The candidate must be an expert in music generation and Schenkerian analysis. The candidate will be responsible for working
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Bioinformatics expertise required for scRNAseq analysis. · Previous cell culture experience. · Perform molecular, cellular, biochemical and immunological analyses. · Optimize and troubleshoot experimental
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. Engage in a spectrum of research activities including literature review, experimental design and execution, data analysis, manuscript preparation, communications with collaborators and journal editors, and
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: • Experience with geochemical speciation methods, particularly synchrotron X-ray spectroscopy • Knowledge of data management • Experience with statistical analysis of data and geospatial analysis tools (e.g
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, engaging with other data analysts, students, post- docs and faculty on the team Conduct comprehensive high-throughput multi-omics data analysis and epidemiological analyses; Apply biostatistics and cancer
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directions that can seed future independent positions. Work Performed Depending on candidate interests and expertise, projects may involve: Analysis of global dietary patterns using genomic approaches
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system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine
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mice HLA-EVL9 antibody biology in PLWH or SHIV infected monkeys Other experiments submitted in your K99 grant Analysis of new HLA-E-VL9 antibodies from the Azoitei Lab Select studies as the budget allows