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Field
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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process. By integrating simulation-informed priors into the reconstruction algorithm, we aim to significantly improve both accuracy and sensitivity. The position is hosted at the Fritz-Haber-Institut
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The Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin, near Bonn, has around 180 employees who research and develop innovative methods in the field of computational
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homogenisation and energy group structure. Investigate the use of AI/ML algorithms to predict or generate cross sections, enabling deterministic solvers to better capture strong heterogeneities and flux gradients
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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real-time? This project will use computational models of neural networks to derive closed-loop control algorithms to modulate oscillatory dynamics in brain circuits. You will test these algorithms
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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lead to a PhD position at SPMS in NTU Singapore (based on performance). Key Responsibilities: Contribute to the development of algorithms Develop codebase in Python and perform large-scale simulations
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use in real manufacturing workflows. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Project