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. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities, ultimately
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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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is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. All qualified individuals
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writing skills. o Proactive mindset and ability to work in a multidisciplinary and collaborative environment. o Adaptability and openness to learn new tools and methods. Language skills
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approach for OFR, building further on existing methods; (2) quantify the value of OFR in Luxembourg ; (3) quantify the impact of forest disturbances on the OFR supply and value; (4) estimate the supply and
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-of-the-art in silico protein structure prediction methods and in vitro screening approaches will be used to systematically elucidate the amyloidogenic potential of the gut metaproteome. Specifically, gene
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into this material and support tailoring its properties. For this, you will: Contribute to method development for ultra-fast MLIPs (Xie et al., npj Comput. Mater., 2023) Develop realistic MD simulation protocols
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will be involved in various international initiatives and engaged with different stakeholders. The candidate is expected to primarily but not exclusively deploy qualitative IS methods, with specific
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live in. Your role This position is inside the SPETRA doctoral training unit which investigates new materials, methods and concepts for converting sunlight into usable energy sources. Inorganic