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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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PhD position in Gene Regulation Faculty: Faculty of Science Department: Department of Biology Hours per week: 36 to 40 Application deadline: 9 September 2025 Apply now Cell identity relies
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matter, that is, the emergent behavior of living materials that can move independently. We will incorporate multi-body interactions such as cell volume energy terms as hypergraphs and higher-order external
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theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
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: 2024). Here, coherent open-path spectroscopy (COPS) will be used for field measurements. In contrast to conventional absorption spectroscopy, COPS eliminates the use of a gas cell and guides the light
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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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Organisation Job description Project and job description This PhD position is dedicated to advancing autonomous robotic manipulation and control within a textile-sorting cell, where garments arrive
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depth. You organise your work efficiently, take initiative, and are able to work independently when needed. You’re open to feedback and eager to learn from it, and you enjoy collaborating within
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technologies. The successful candidate will have strong data-driven methodological learning opportunities with high social impact on cancer care organisation. They will work within an interdisciplinary team