10 learning "https:" "https:" Postdoctoral research jobs at Delft University of Technology (TU Delft)
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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requirements We will consider applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). A PhD in aerospace/mechanical
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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., in Python. A keen interest in interdisciplinary research. An excellent command of English, as you’ll be working in an internationally diverse community and with international partners. Ability to learn
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and
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, subjective user feedback, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control
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with programming and scientific software development; experience with C++ is an advantage. Experience with machine learning, 3D city modelling or urban reconstruction techniques is beneficial. You are
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also provides funding for professional development by following two Advanced Courses offered by BioTech Delft (https://biotechdelft.com/), a membership for the Dutch Biotechnology Association that allows
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. Your home base will be the Jakobi Lab (https://cryoem.tudelft.nl) at TU Delft | Kavli Institute of Nanoscience. We are a multidisciplinary, international and diverse team of scientists with backgrounds
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executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi