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a powerful way for assessing forest stress and disturbances over large areas and to monitor forest vitality over time. This research uses remote sensing technologies together with physical models and
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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-pathogen interactions using in vitro model systems mimicking chronic diseases. The project foresees ample collaborative opportunities with research groups in the MICRO-PATH consortium, spanning
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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studies and interactive AI systems. This position will be funded based on an initial 2-year contract + 2 years extension. The key idea is to apply theories, models, and methods from psychology to improve
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About us The School of Neuroscience is UK’s 2nd largest Neuroscience school with over 500 researchers and 200 PhD students. It is one of three schools at the Institute of Psychiatry, Psychology & Neuroscience. CDN is one of four departments in the School of Neuroscience at the Institute of...
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technologies including thermal energy storage (TES) and hydrogen technologies integrating with concentrating solar power (CSP). Responsibilities under this position include leading research work on modeling
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Program (PRR Workplan: (1) Numerical simulation of the behaviour of connections between prefabricated concrete walls using finite elements; (2) Case study analysis; (3) Processing and analysis of results
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Josip Juraj Strossmayer University in Osijek School of Applied Mathematics and Informatics | Croatia | 1 day ago
field of mathematics, for work on the project Croatian Science Foundation „Scaling in stochastic models“ (Project ID: DOK-2025-02-9144), according to the tender Croatian Science Foundation „Razvoj
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description Specifically, this project combines high-throughput experimentation, synthesis of model catalysts, operando characterization, and molecular modelling to identify novel catalyst families and develop