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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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short-term physiological responses of tree species and modified long-term dynamics of the whole ecosystem. On the other hand, vegetation demography models are numerical tools formulating forest processes
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, biochemists and technicians. The multidisciplinary research in our group covers a wide range of topics from radiobiology, radiation physics and space research to radiation therapy. The radiobiological modelling
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, and state-of-the-art infrastructure. Our model empowers visionary leaders to accelerate drug development and harness AI-driven innovation to address unmet medical needs. Benefits of Working with Great
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an experimental team, with direct availability of experimental validation for machine learning models. Competitive salary and full benefits. Access to state-of-the-art computing infrastructure. Fully funded for 4
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put