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acousto-structural transmission paths, developing predictive models, and producing research outputs that support practical noise mitigation solutions for the built environment industry. Key Responsibilities
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago
profiling (BGC-Argo) floats (https://www.nature.com/articles/s41586-021-03805-8). The NASA Ocean Biogeochemical Model (NOBM) has recently been coupled to the Subseasonal to Seasonal Prediction Version 3 (S2S
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predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results. Where to apply
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. For the Working Group “Ecosystems in Transition” in RD1 at PIK, we are offering a PhD position (m/f/d) (Position number: 10-2026 PhD ARTECO) in the field of ecological modelling of complex arctic landscapes
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. The candidate will reduce model uncertainties by producing new large cosmological simulations of the magnetic outputs from galaxies in the ENZO code, which will test realistic implementations of baryonic feedback
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17 Mar 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Modelling tools Geosciences » Geology Geosciences » Hydrology Researcher
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in New York, across the nation, and around the world. Position Function The CNCPS was modified to be able to predict nitrogen (N) and phosphorous (P) excretion, and this was published by Fox et al
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Objectives: - Completion of the characterization of nanomateriais on in vivo models of vaccination - Completion of the characterization of nanomateriais on in vitro and in vivo cancer models - Analysis
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and