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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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data. Leading statistical analysis (methods may include IPTW-weighted Cox regression, active comparator new-user designs, self-controlled case series, propensity scoring). Developing and extending an AI
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics) • Multi-omics for non-cancer health screening applications, • Machine learning modelling
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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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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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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
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
on the Applied Federated Energy Research Accelerator (AFERA), a novel framework for end-to-end applied energy research integrating Agentic AI, Applied Energy Solvers, Digital Twins, and Control & Automation
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an impact on climate via cloud formation. The MACS project aims to understand the role of mixotrophic protists in Antarctic C- and S-cycles. Such mechanistic understanding will help predict how changing ice
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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