261 algorithm-development-"Multiple"-"Prof"-"UNIS"-"DIFFER" PhD positions in Netherlands
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- University Medical Center Utrecht (UMC Utrecht); Published 31 Oct ’25
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- University of Twente (UT); today published
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- Vrije Universiteit Amsterdam (VU); Published today
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- Wageningen University & Research; Published 21 Nov ’25
- Wageningen University & Research; Published 28 Nov ’25
- Wageningen University & Research; Published yesterday
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by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool for CP diagnosis and long-term outcomes. This tool will integrate advanced clinical
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multiple sclerosis—leads to impaired movement, unstable gait, and reduced quality of life. This project will develop an adaptive stimulation interface that uses real-time position-sensor data to estimate
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Application deadline: 5 January 2026 Apply now Do you want to develop predictive models that integrate omics and environmental data, advancing the field of precision prevention? Join the Institute for Risk
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD project aims to develop a
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developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize
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4 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science Mathematics » Algorithms Mathematics » Statistics Researcher Profile First
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well as to optimize the tooling geometry. These process simulations require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will: Develop efficient
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transparency and trade secret claims of regulated actors? And explore legal arguments in support of algorithmic transparency and data access for public interest research? How does EU law balance transparency and
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL