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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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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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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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genomics, metabolomics, or microbiome analysis Computer science, particularly machine learning, artificial intelligence, data science, or computational biology Mathematics or statistics, with experience in
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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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application to medical imaging (e.g., MRI) · Experience with MRI data analysis, network science, graph theory, topological analysis, or related computational approaches, especially in Alzheimer’s
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with Dr. Kate Hoffman to define and lead specific research objectives aligned with the funded aims. Responsibilities will include project management, coordination of data collection and analysis
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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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, Bioengineering, Biophysics, Biostatistics, Mathematics, Statistics, Electrical Engineering, Biomedical data science, etc; Candidates with multidisciplinary backgrounds are also welcome. Strong skills in
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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