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, advanced high-parameter flow-cytometry, as well as murine models and human organoid technology to investigate mechanisms of longevity of immunological T and B cell memory. A strong interest in quantitative
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-parameter flow-cytometry, as well as murine models and human organoid technology to investigate mechanisms of longevity of immunological T and B cell memory. A strong interest in quantitative disciplines
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collected by moored probes, underwater gliders and micro-AUVs and shipborne sampling. Furthermore, parameters describing atmospheric variability and riverine inputs will be included in the analysis to better
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal
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biodiversity and restoration. As a Research Assistant, you will contribute to case studies focusing on key environmental parameters, including surface temperature, soil moisture, water table levels and total
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timely and efficient troubleshooting of the majority of systems during user image acquisition. Help users to switch instrument parameters, allowing users to optimize their experiments real-time. Ability
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– DeepEruptive: AI for eruptive parameter estimations (petrology + CFD + tephra maps) Host/PhD: Sorbonne University (France) | Supervisor: Prof. Paola Cinnella Secondments: INGV (Italy); Ainoudo (France) DC9
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 5 minutes ago
improve estimation of rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability. We seek projects focusing on the use of machine
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engineering What you will do Develop grey-box models that combine first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification
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first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate experimental