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doctoral projects will work on enzyme design, bioprocess design, and machine learning tools to create sustainable bioprocesses. PhD project In this project, you will work on designing a scalable bioprocess
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for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: 1. Probabilistic and differentiable algorithms for machine learning; 2. Programming language
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this position, the chosen candidate will conduct research on how machine learning techniques or XAI can be leveraged by heuristic algorithms, or conversely, how heuristics can be enhanced by incorporating machine
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programming applications (e.g., experimental design, machine learning for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: Probabilistic and
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for cognitive science and artificial intelligence, including about 35 PhD students. Core research domains include cognitive science, machine learning, deep learning, games, virtual reality, computational
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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engineering Engineering » Design engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 5 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable
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will work on enzyme design, bioprocess design, and machine learning tools to create sustainable bioprocesses. PhD project In this project, you will work on designing a scalable bioprocess using
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, Department of Mathematics and Computer Science. The student’s working locations will be at Eindhoven University where the PhD candidate will be supervised by experts in combinatorial optimization, machine
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at the Institute for Computing and Information Sciences (iCIS), part of the Faculty of Science at Radboud University . The Data Science Group comprises around 50 researchers with expertise in machine learning