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FLOW research group is a young, dynamic group working in the fields of thermodynamics, fluid mechanics, and data-driven modelling. At the Department of engineering Technology (INDI) — Thermo and Fluid
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astrophysical transients (including kilonova), nuclear structure, nuclear reactions, fission, nuclear isomers, nucleosynthesis, and state of the art simulations. LANL scientists working in these areas include
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the solidarity and cooperation within and between democratic countries. There have been concerns about the use of Generative AI and Large Language Models (LLMs) as tools for the dissemination of disinformation and
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tools (e.g., drones, 3D mapping) for high-resolution geological mapping and rock mass quality assessment. Develop and calibrate numerical models using field data and case studies to simulate various
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of the infrastructure, design and execution of large‑scale measurement campaigns, and development of data‑driven models for room acoustics and spatial‑audio. The specific research direction will be finalised after
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challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation capabilities. Recent
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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: Coordination Layer: Formulate passivity-based conditions that guarantee agents—modelled as general nonlinear systems—synchronize their outputs or follow desired collective patterns purely through local
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(https://grad.uiowa.edu/postdocs/salary). Applicants must be eligible to work in the United States. Education Requirement: Completion of Ph.D. or M.D./Ph.D. in cancer biology, tumorimmunology, or similar
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+ Quantum Information Science + Quantum Optics + Theoretical Physics , Quantum Sensing , Quantum Sensors , Quantum Simulation , Quantum Technology , Quantum Transport , Quantum transport in low-dimensional