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information For further information, please contact Prof. Ants Kallaste ants.kallaste@taltech.ee and Prof. Anton Rassõlkin an- ton.rassolkin@taltech.ee or visit https://taltech.ee/en/electrical-machine-group
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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goals Recent developments in autonomous driving have shifted toward E2E pipelines that unify perception, planning, and control into deep learning–based architectures. These models enable flexible decision
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highly motivated and ambitious PhD candidate with experience in either biomedical engineering, machine learning, polymer technology, physics, electrospinning, or similar fields,to join our Lab- on-a-chip
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data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research findings
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to the European project UPCHANGE (https://upchange.solutions ), which during the next 3 years will develop solutions for integration of renewable energy generation systems in the built environment, digitalization
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. To this end, the candidate is expected to have a good knowledge of programming tools and acquire knowledge about our custom systems during the initial stage of the doctoral studies. Responsibilities and