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- Delft University of Technology (TU Delft); Published 21 Nov ’25
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and
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a strong background in computational modeling, optimization, and systems engineering, eager to develop advanced design and simulation frameworks for battery pack topology and e-powertrain integration
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Recycling (MPRR) group in the Department of Materials Science and Engineering (MSE) at TU Delft and contribute to advancing battery circularity by optimizing the recovery of critical raw materials in
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optimizing the recovery of critical raw materials in the hydrometallurgical recycling of Li-ion batteries. As a PhD researcher, you will: Investigate purification and separation methods to understand
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), , Logistics Overijssel and Logistics Navigators. The academic supervision team includes Dr. Engin Topan and Dr. Rob Benthuis. MULTIPLIER integrates operational data, optimization, and hybrid AI into a decision
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capabilities, existing technology can only handle relatively small-scale problems. Information In the SymBi project (Exploiting Symmetries for Faster Bilevel Optimization Algorithms), we address this limitation
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demand for heat and heat generation from sustainable sources. Optimizing sustainable heat requires storing large amounts of heat to account for seasonal supply and demand fluctuations. Various technologies
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interdisciplinary meetings and workshops to foster knowledge exchange and innovation. Your work is vital to advancing less invasive treatment options, reducing patient recovery times, and optimizing healthcare
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(PICs) increasingly used in various applications. To allow a smooth design flow for these PICs, optimized compact models are needed. This PhD position is enabling compact models for optical amplifiers and
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on the results of the Erasmus+ project EDDIE. When it comes to the research part: This PostDoc project explores how artificial intelligence (AI) can be leveraged to optimize energy portfolios for local energy