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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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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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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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that makes that reuse possible at scale — not just once, but sustainably, across the research community? The Davis and Krishnan labs work at the intersection of these questions. We develop ML/AI methods 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
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evaluating the effectiveness of various recharge methods. A specific focus of this component is understanding subsurface structures that can facilitate focused recharge (e.g. sinkholes, losing/gaining streams
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of microscopy techniques, microfluidic approaches, and single-cell analysis methods to investigate quiescence at individual cell and population levels. Where to apply E-mail dominika.wloch-salamon@uj.edu.pl
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tractability, decomposition methods), learning-enhanced approximations (e.g., embedding-based representations of complex interactions, passenger flow estimation using generative techniques), and artificial
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cancer progression. Knowledge of research techniques or methods (cell biology, translation, transcription, primary cultures, microscopy, cell assays, biochemical assays, molecular biology assays
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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