72 parallel-computing-numerical-methods research jobs at Pennsylvania State University
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SPECIFICS The Uzun Lab at the Penn State College of Medicine, Department of Pediatrics, Hershey, PA, is seeking a postdoctoral scholar in Bioinformatics/Computational Biology. Our lab’s research interests
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collaborate closely with CCMA faculty on cutting-edge research projects involving design, analysis and implementation of advanced numerical methods and algorithms in interdisciplinary applications. The position
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SPECIFICS The Uzun Lab at the Penn State College of Medicine, Department of Pediatrics, Hershey, PA, is seeking a postdoctoral scholar in Bioinformatics/Computational Biology. Our lab’s research interests
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Coursework or project experience in bioinformatics or computational biology Basic programming skills in Python, R, or similar languages Willingness to learn new computational tools and methods Strong attention
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computing, applied mathematics, or computer science. The position is ideal for someone with training in numerical methods with a strong background in mathematical biology and biomedical applications
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quantum information science. Specific topics of interest include dynamics in closed and open quantum systems, digital and analog quantum simulation with quantum computers or atomic-physics platforms, and
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Ph.D. and have exceptional research potential. Teaching may be required. Qualified candidates are expected to have a background in scientific machine learning, numerical analysis, and dynamical systems
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their own research program in collaboration with, and in parallel to, Prof. Zanazzi. Penn State hosts a vibrant community of scientists working on many aspects of exoplanetary astrophysics, including
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The College of Agricultural Sciences, Fruit Research and Extension Center located in Biglerville, PA seeks to hire numerous individuals to fill part-time positions. Positions are 40 hours per week positions
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scholars. The overall goal is to: (1) develop and apply statistical genomic methods to analyze multi-omics datasets for understanding complex disease etiology and (2) develop and apply novel statistical