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Field
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modeling, or real-time data analysis. Familiarity with data visualization, reproducible research workflows, version control, and collaborative coding practices. Interest in translating computational methods
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Bring physics into world models — and make embodied agents fast, reliable and ready for the real world! Join us! World models are controllable, physics- and mechanism-grounded simulators of reality
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
modeling (ROM) Some experience with various programming tools (Python, MATLAB, C++, C) Some familiarity with machine learning: predictive modeling, anomaly detection, supervised learning, deep learning
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a
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for the diagnosis and prediction of lithium-ion battery ageing. About us At the department of Electrical Engineering research and education are performed in the areas of Systems and Control, Communications, Signal
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | about 2 months ago
regulation of oncogenic competence and drug sensitivity. This project develops machine learning models (sequence-to-function, cell state model, etc) to predict gene regulatory and cell state changes under
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of experimental and/or simulation data, the development of predictive models linking cellular responses to the properties of the surrounding environment, and the implementation of explainable AI approaches
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very low; Propose characterisation of the soil properties collected from different studied farms; Test how to Improve soil organic carbon content using organo-mineral resources under controlled condition
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surface temperature) with eddy flux measurements to scale up findings and develop predictive models of water use efficiency, and c) quantify water use efficiency and its temporal and spatial variability