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sampling-based and reinforcement learning-based motion planning algorithms for multiple robotic arms in automotive manufacturing, including testing, performance evaluation in both simulation and actual
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mission standards. Core challenges: translating advanced algorithms into field deployable and operational systems managing complex technical integration across multiple consortium partners and international
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algorithm that reliably simulates two-phase flow. The PhD projects will be part of developing and analyzing relevant numerical methods and implement them in an open-source framework that will be made openly
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control using deep learning. Implement and test new algorithms in actual robot platforms. Job Requirements: PhD in Electrical and Electronic Engineering or related field. Hands on research experiences in
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in a previous PhD project. In addition to electromagnetic geophysics, the candidate is expected to contribute to the development of novel workflows for joint inversion of multiple data types (e.g
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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multiple long-term conditions navigate their social care needs. The position is full-time fixed-term until 01/04/2026. About you You will hold a PhD or equivalent professional qualification and experience
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multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and their dynamics. Conducting literature searches, manuscript preparation, and
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve