24 computer-vision-and-machine-learning PhD positions at Utrecht University in Netherlands
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in the SPG. We will make use of models of different complexity up to complex Earth System models, and modelling efforts for different past periods. A personalised training programme will be set up
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contribute to the structural characterization of NS3–inhibitor complexes Process and analyze cryo-EM data using established computational pipelines and structural modeling tools Apply, or develop expertise in
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are encouraged to submit a research proposal that aligns with UCALL's research programme and encompasses multiple areas of law. Your job Over a period of four years, you will conduct a PhD research under the
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significant controversies regarding the exact mechanism of action. To understand the molecular and biophysical principles of protein folding, we aim to build fully controllable chaperone-like machines using DNA
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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.); computational skills to analyse social media data (e.g., with Natural Language Processing, LLMs), and/or a strong motivation to learn these skills; excellent oral and written command of Dutch and English
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Apply now Are you passionate about the future of education and AI? Join us as a PhD candidate to explore how adaptive AI can enhance innovative teaching and learning. This interdisciplinary project
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excellent English oral and writing skills and willingness to learn Dutch. Our offer A position for 1 year, with an extension to a total of 4 years upon a successful assessment in the first year, and with
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and AMOC changes at decadal to millennial timescales. This project may include participation in seagoing expeditions. This project is part of the 10-year EMBRACER research programme funded by the Dutch
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health behaviour. Using a novel combination of deep learning, street view imagery, and epidemiological methods, we aim to identify the most effective urban exposure modifications. This research will