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with leaders in the domain of infrastructure governance and modelling. You will learn to conduct research independently and grow scientifically through collaboration with researchers from Radboud
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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hands-on experience with microwave-based diagnostics. You have a proven track record in mentoring and supervising early-career researchers and have demonstrated successful collaboration with peers in your
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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spanning neuroinflammation, machine learning, and single-cell biology. Opportunities for co-authorship on high-impact publications and involvement in grant writing and collaborations. Flexible work
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Containers (e.g. docker). * Proven ability to work independently and collaboratively. * Experience with communicating results and concepts with a diverse audience including molecular biologists and laboratory
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with the opportunity to complete a high-quality dissertation participation in a dynamic research team with supportive colleagues, fostering collaboration and mutual learning l a lively community of PhD
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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strategies, including hospital wastewater treatment solutions. Collaborate with multidisciplinary hospital teams and external partners. Publish findings in high-impact scientific journals and present