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of traumatic extremity injuries and amputations with a specific focus on translating their findings into clinical practice to improve the care of injured Service Members and Veterans. To learn more, visit: https
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: combining microscopic modelling and simulations (e.g., Brownian dynamics) with continuum approaches (e.g., statistical field theory) to study the macroscopic behaviour of proliferating active systems such as
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Institute’s mission and this position. Responsibilities The University/Huntsman Cancer Institute (https://healthcare.utah.edu/huntsmancancerinstitute/) is a vibrant community, home to exceptional epidemiologic
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participatory mapping, interviews, ecological fieldwork, ethnography, arts-based methods and relation-based simulation modelling in transdisciplinary processes in order to examine ecological and socio-spatial
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strategy (PMIDs: 29123070, 33621493, 33087936, 30566856, 39947938; doi: https://doi.org/10.1101/2025.03.15.641049 ). Postdoctoral Projects Project 1: Replisome Dynamics, Replication Stress, and Cancer
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of the applications: 2.1 — The Applications must be accompanied by the following documents: a) Application minute for the competition addressed to the Rector of the University of Aveiro; b) Copy of detailed curriculum
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interactive media, to support faculty and student research in games, simulation, and XR environments. 2.Support faculty in the development and deployment of the front-end and back-end components of a macro
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of recommendation Brief statement of research interests (1500 characters) Copy of your original doctoral degree certificate Application deadline: applications can be submitted until March 1, 2026, evaluation will be
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scientists and companies working in liposome technology, cell-free protein production, sensor development, clinical nephrology, and medical decision-making. Where to apply Website https
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative