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candidates from all backgrounds to join our community. At the Department of Applied Physics, our pioneering research in physical sciences creates important industrial applications that hold great technological
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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both homogenous and heterogenous catalytic conversion, including the handling, testing and characterizing the catalysts as well as the reactor and process simulation and analysis. Details will be
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(matti.kuittinen@aalto.fi ). For questions related to the application process, please contact HR Partner Enni Ailoranta hr-arch@aalto.fi . We will go through applications, and we may invite suitable candidates
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems