155 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs in Netherlands
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- Delft University of Technology (TU Delft); Published yesterday
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outcomes under different market design scenarios. The research will combine machine learning, stochastic optimization, and agent-based modelling with behavioural experiments. Case studies from emerging
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leader and follower decisions. We are seeking for a motivated and enthusiastic Postdoc (PD) researcher to join the Combinatorial Optimization group (https://www.tue.nl/en/research/research-groups
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to develop synthetic cells that mimic tubular function by integrating transport proteins in lipid membranes. Furthermore, the ultimate long-term goal is to integrate these cells in a dialysis machines. The
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, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process
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group? Do you enjoy creating complex machines that have never existed before? Do you want to explore physics that nobody else has seen? If yes, then you might want to join the Ultracold Strontium Group
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the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a passion for developing the teaching and learning of green hydrogen skills? In that case, please
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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and analytical outputs for stakeholders. Support stakeholder engagement activities (workshops and pilots). Contribute to academic publications based on the project’s work. Where to apply Website https
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-generated scenarios or machine learning-driven attack/defense strategies; Experience in developing comprehensive security assessments, producing technical reports, and contributing to toolkit documentation
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opportunities for co-developing multispecies governance approaches with representatives of governance bodies and identifying ways to transform towards them. Finally, we will synthesize our learning across cases