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transcriptomics data and network-theoretic approaches. - design of a new mathematical method - monitoring and study of publications relevant to the field - programming/coding in Python (Pytorch) - presentation
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, genomic selection modeling, bioinformatics, proficiency in R, Python and/or Linux command line • Experience with DNA/RNA extraction and library preparation • Experience working with large-scale datasets
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programming skills (e.g., C, C++, Python), familiarity with Linux Proficiency in English and ability to work in a team Outstanding analytical and problem-solving skills Employment terms: The successful
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, or air pollutants. Our future research strategy also includes studies of the higher atmosphere. To learn more about our team, we invite you to visit our website: https://flexteam.univie.ac.at/ . To learn
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fundamental questions about soil health in agricultural cropping systems and advance your research career in a leading international research environment. The position is part of the EU project MultiSoil (https
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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https
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cover letter describing research interests, a current curriculum vitae, and contact information for three references – https://unmc.peopleadmin.com/postings/95260 . Required Qualifications Ph.D
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
system and its components. ISSM is a modular C++ codebase with MATLAB and Python interfaces, designed to simulate Cryosphere, Solid Earth, and Sea Level processes, either in coupled or standalone
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quantitative genetics or animal breeding Has published high-quality research in peer-reviewed journals Experience with scripting languages (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with