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, different time scales of predictability. Famous examples include various states in the transition to turbulent fluid flow or metastable chemical configurations. However, such transient stochastic phenomena
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predict the impacts of regenerative agriculture spanning - biophysical, social, economic. At the farm level, these insights are crucial for developing and implementing effective business models. At
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-GUIDE project, we will make directed evolution guidable and, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity
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want to contribute to the bottom-up construction of synthetic cells? Are you excited about the application of AI tools to predict gene expression levels across a synthetic genome? Then join our team as a
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mixing using pipettes/scales, and handling instruments and equipment. You have a quantitative mindset with the ability to analyse data, make predictions, and perform back-of-the-envelope calculations. You
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. Important themes are logistical organisation of regional care and prediction of treatment outcomes for individual patients. Research activities involve collecting (prognostic and care logistics) data
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measurement data for translating and testing model predictions; bioinformaticians, who investigate evolutionary conservation of sequence, (co)expression and regulatory modules; and modellers, who develop crop
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, or dynamic models to predict gene regulatory interactions. Work with digital twin technology, simulating patient-specific disease progression and treatment responses. Collaborate in an interdisciplinary
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on Education and AI (NOLAI). You will create methods to predict energy consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI
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, perform experiments, and develop prediction models. You will focus on the effect of deconstruction on the structural strength, stability, and fatigue life of reused components. Your goal is to develop a set