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University of California San Francisco | San Francisco, California | United States | about 1 month ago
complex analytical studies, interpreting results, and managing large datasets using statistical programming languages such as SAS, SQL, R, or Python. Experience with data visualization tools such as
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metabolite analysis You have good skills in the use of R for statistical data analysis and Python programming Any teaching experience or participation in extra training, conferences, etc. is recommended You
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TASKS The Marchal lab (IDLab/IMEC Ghent University, https://idlab.ugent.be/people/802000961346 ) has an open PhD position within the Marie Sklodowska-Curie Doctoral Networks (MSCA-DN) project iSTRIDE
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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. The salary range for this position is $25.72 - $36.59 (Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu
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the application which project you are specifically interested in. Further details on the projects can be found here: https://www.fz-juelich.de/en/jcns/careers/fellowships/tasso-springer-fellowship-program Your
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(Python, C) and computer-aided omics approaches (transcriptomics, RNA imaging), as well as pathway modeling, are highly desirable. Specific Requirements Microscopy (live and fixed tissues); computer-aided
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/statistics, operations management/supply chain, python programming, computer networking, systems admin, digital innovation, systems analysis & design Optional (if available): Recent teaching evaluations
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by Renew Risk Ltd. (https://www.renew-risk.com/ ), offering opportunities to work with real offshore wind farm models and industrial datasets while addressing real-world challenges in collaboration
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computational modeling, which is a collaboration between Dr. Brennan, and Dr. Edith Kaan at the University of Florida. The project description is available at: https://www.nsf.gov/awardsearch/showAward?AWD_ID