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Field
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audience. Skill in modeling the collective behavior of complex systems, understanding how micro-level interactions drive macro-level structure and evolution. Practical experience with high-performance
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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) protein dynamics; 2) molecular dynamics simulations; and 3) high-performance computing environments. Prior programming experience is preferred but not required. The postdoctoral scientist will design
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the following are required: • Excellence in structural biology • Excellence in programming and high-performance computing • Excellence in bioinformatics • Excellence in evolutionary theory Additionally
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opportunities for high-impact publications and grant development. Key Responsibilities Perform computational analysis of large-scale omics datasets, including proteomics, transcriptomics, and related modalities
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documentation for teaching bioinformatics. Prepares survey reports, summaries, lesson plans, and perform quantitative analyses of outcomes. Contributes to preparation of scientific articles reporting program
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experience is required. If the applicant graduated more than 1 year ago from a PhD or MD training program, minimum of one (1) year of experience with high throughput biomedical or neuroscience research
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towards improving controllers in turbulent flows. On any given day, you may be called on to: Own and execute novel machine-learning workflows related to wind conditions and control performance. Post-process
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independent and collaborative theoretical and computational research in quantum condensed matter physics Develop and implement high-performance scientific software, and simulation tools Publish research papers
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
Postdoctoral Positions: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and Human Genetics