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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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biological mechanisms is necessary to establish mechanistic models for tree distribution and growth that will improve our predictions of tree species' range-shifts as well as productivity changes within
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questions for both Biogeography and Quaternary Palaeoecology, and the answers provide the basis for predictions of ecosystem and species response to future climatic change. We are looking for PhD candidates
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Law, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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Negative Thoughts’ A World-Class Research Environment at the Intersection of Neuroscience and Technology The Neural Control of Movement Lab led by Prof. Dr. Nicole Wenderoth and Brain-Body Regulation Lab led
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judiciary Have experience with large language models, information retrieval, evaluation of ML systems, and deploying applications; this is highly desirable Have an excellent command of English; German
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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of velocity models using controlled test events and quality control of data acquisition Algorithm Development: Integration of multi-sensor data streams into seismological software frameworks (e.g., SeisComP
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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phenotyping research focuses on the development of methods and models to determine traits of relevance for crop breeding and variety testing. The Agroscope research group Extension Arable Crops (Competence