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to develop an innovative model to estimate the direct and indirect costs of urbanization based on urban morphology (compact or sprawling). The candidate will analyze real case studies, integrate variables
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We are seeking to recruit a 2-year postdoctoral researcher within Human-Computer Interaction to work on the interdisciplinary project DARES: Data Quality for Responsible AI in Energy Systems funded
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
medium. Methods of investigation may include analyses of archival multi-wavelength observations from space- and ground-based facilities, as well as new observations and analyses. Additional interests
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of working on a computer Advantages: active participation in scientific research work, training, recognition and awards for excellence, experience working in a cell laboratory, experience working with PCR
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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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projects as well as third cycle courses, seminars and conferences. You will perform standard physics-based methods to estimate absolute and relative binding free energy. Based on these, you will develop
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, Microsoft Outlook Preferred Other Computer Applications: Preferred Additional Knowledge, Skills and Abilities: Email to a Friend https://unmc.peopleadmin.com/postings/97047
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: Department of Family Medicine Course Title: Applied Mixed Methods in Health Research Course Code: FMED 672 Estimated Number of Positions: 1 Total Hours of Work per Term: 20 Position Summary: Family Medicine
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of paleomagnetism, with a particular focus on the application of magnetic methods in paleogeographic studies publishing research results in peer-reviewed scientific journals supporting early-career researchers and
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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces