92 parallel-and-distributed-computing-phd research jobs at Pennsylvania State University
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lab. The lab focuses on developing and implementing new computational and artificial intelligence methods in genomics and molecular biology. Current projects include the identification of genetic
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a background in computer science and programming. The ideal candidate will have strong skills in web scraping, processing unstructured data (both text and images), and experience with modeling
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laboratories of Dr. Juliana Vasco-Correa and Dr. Christine Costello on multiple funded research projects, performing computational modeling of crop and livestock production and forestry systems, and
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atmosphere. Examples of such processes include the cycling of trace gases and water vapor in the Amazon rainforest. Figures need to be created to display spatial and temporal patterns of trace gas distribution
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focus and interest. Fellows will work directly on research projects of CGNE-affiliated faculty, pursue independent research activities in line with their developing program of research, and engage in
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: Your role at the lab is to investigate the behavior of individual molecules and bio-molecular condensates in living cells. You will use and develop the imaging and computational methods that to visualize
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journals and present at academic and professional conferences. Mentor graduate and undergraduate students, including guiding PhD students in their research. Collaborate with faculty and other researchers in
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REQUIREMENTS: The candidate will work with the Project Director to create detailed 3D models of industrial piping systems and plant layouts and perform Computational Fluid Dynamics (CFD) simulations to analyze
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tools (e.g., light sheet fluorescent microscopy) and computational approaches to examine cellular resolution details in the whole mouse brain. Moreover, we use systems neuroscience approaches (e.g
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Sriperumbudur. Potential research projects include (but are not limited to) developing theory and methods for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows