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requiring the fellow to be in person at partner sites and on campus (e.g., data collection, school-based consultation, grant convenings). This project has three primary aims: Recruit, retain, and mentor
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scientific methods, will have opportunities to collect data and conduct statistical analysis of large-scale data. Qualifications Basic qualifications • PhD or ScD in Health/Public Policy, Public Health
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information about our research group can be found at: https://autonomousrobots.nl/ Job requirements We are looking for a candidate with: A PhD degree in Robotics, Control, Aerospace Engineering, Mechanical
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, radionuclides, 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
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intelligence methods (ML/AI) for diverse biological data in relation to cardiovascular and neurodegenerative diseases. The specific areas of methodological research can be flexible, depending on the candidate’s
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
of organizations and communication. The course develops students’ understanding of organizational dynamics and their ability to navigate social networks, work in teams, interpret incentives, leverage collective
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immigration enforcement. Dr. Young is seeking a scholar with demonstrated experience in qualitative data collection and analysis and analysis of survey data, with a preference for candidates with a strong
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experiments, etc. Perform data collection and analysis Test, optimize, troubleshoot new and existing methods and techniques, and update protocols Prepare and maintain biological stocks Maintain proper lab and
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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk