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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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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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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
The successful candidate is expected to: Build and evaluate chemical databases based on experimental and computational data. Establish predictive models based on artificial intelligence methods. Utilize
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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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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
and modeling of RESOLUTE data (https://re-solute.eu/ ) together with metabolic, structural, genetic and clinical datasets to generate new insights into transporter biology: how cells regulate access
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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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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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