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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning
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) List of award winners Our sponsorship The award amount is €45,000. Award winners are also invited to conduct a research project of their choice at a research institution in Germany in cooperation with
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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open up exciting career opportunities? Are you interested in cable technology and condition monitoring and do you have a strong competence in signal processing and machine learning? As a PhD candidate
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criteria Demonstrated knowledge and experience in electrical system modeling and analysis, applied control, power electronic systems, optimization techniques, and/or machine learning. Experience with
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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livable cities. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML
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. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML) methods to tackle