31 parallel-computing-numerical-methods-"Prof" positions at Nature Careers in Luxembourg
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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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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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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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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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SD-25157 RESEARCHER IN ATMOSPHERIC PLASMA TREATMENT OF METALLIC SURFACES FOR INDUSTRIAL APPLICATIONS
Treatment of surfaces. Good knowledge about Plasma characterisation with state-of-the art methods. Good knowledge about Surface characterisation with state-of-the art methods. Demonstrated experience in
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charge of: Developing surface preparation methods and thin coating deposition methods Characterize thin coatings Manufacture ultra-thin materials assembly and characterize their interface Understand
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the development of both, the quantum internet and distributed quantum computing. The objectives of this PhD thesis project are: (a) Demonstrate spin-photon entanglement with single colour centres in silicon carbide
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publication record. Experience in formal and computational argumentation is a significant asset. Demonstrated ability to apply AI methods to domains such as law and finance. Experience in developing hybrid AI
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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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numerical solvers (GLPK, HiGHS, CPLEX, Gurobi) for investment planning and operational analysis. Experience with dynamic or co-simulation environments (e.g., combining electric, thermal, and control modules