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the field of analytical and/or simulation methods for composite materials. You have extensive experience with development and implementation of algorithms for modelling of composites You have experience with
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): Prof. Pia Fricker, specialist in computational design and adaptive landscape and territorial systems. ADAPT PhD researcher, focusing on environmental data analysis and prototype development
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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(“Autonomous System for Hybrid Hyperspectral-SAR Monitoring in Precision Agriculture”, Supervisor Prof. Hugo Hernández Figueroa, and “Development of Methodology and Robust Operational Algorithms for Hybrid
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Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies that transform dynamic environmental processes
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 8 days ago
Lattice Codes” (BoLaCo) project, a French-German collaboration funded by the ANR and the DFG, in partnership with Prof. Jens Eisert’s group at Freie Universität Berlin. The project aims to develop new