56 phd-drug-discovery "Eindhoven University of Technology (TU" positions at Delft University of Technology
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our washing machines, which cannot be completely filtered with current methods. We are working with an industrial partner to find a solution to the problem. In this project, the PhD researcher will
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this research area, and has numerous working setups and researchers working in this area. Our overarching goal is to help the global community upscale the technology towards practical application. The open PhD
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are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €2541 per month in the first year to €3247 in the fourth year. As a PhD candidate you will be enrolled in
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biological phosphorus removal. Change: Evaluate bioreactor operations using cutting-edge omics techniques Impact: Improve phosphorus removal and recovery in wastewater treatment processes The PhD project aims
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research visits: Visit to Prof. Yasaman Ghasempour's lab at Princeton University for 5 months Visit to Nokia Bell Labs, Stuttgart for 3 months. This PhD position focuses on "robust mmWave communication and
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. Trustworthy recommender systems are those that not only provide accurate and relevant recommendations but also prioritize these principles to foster user trust and confidence. We invite prospective PhD
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research visits: Visiting Dr. Yang Miao at KU Leuven for 5 months Visiting National Instruments (NI), Dresden for 3 months. In the context of this PhD position, you have the chance to conduct a comprehensive
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model complexity and function approximation. You will be welcomed into our Sequential Decision-Making group, where we focus on various aspects of reinforcement learning. During your PhD, you will have the
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of describing cloud processes in models. You will join the dynamic climate science community at TU Delft with many opportunities to engage with other PhD researchers across different disciplines. Within your PhD
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and respond to such crises. As a PhD student at TU Delft, you will conduct research and develop non-Markovian models from the viewpoint of Network Science and geared to predicting and preventing spread