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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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, to characterize immune cell dynamics in murine models of inflammation and cancer. RESPONSIBILITIES: Developing and performing computer simulation of MRI contrast of labelled cells and tissue Labeling and tracking
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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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the fields of microsystem engineering, microdevices and microfluidics, intelligent systems programming and modeling, autonomous control and computer vision are preferred. The successful candidate should be
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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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mechanisms. The overall goal of the research project is to develop process understanding and parametrizations that lead to improved, energetically consistent, climate models. Close collaboration with the other
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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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Via Multiple Noncovalent Interactions” in the second funding phase at the Martin Luther University Halle-Wittenberg will start with a highly interdisciplinary and ambitious research program in November
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below? Are you our future colleague? Apply now! Experience and skills · You have a strong interest in terrestrial ecosystems modelling, vegetation demography, plant physiology, and climate change