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for use in health science. The goal of this PhD project is to expand on these computational methods and their formal foundations and to create efficient algorithms and implementations of them. We
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more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation
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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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training unit: https://www.list.lu/en/research/project/forfus Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? Your PhD work will focus on outdoor 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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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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· PhD in Aerospace Engineering, Mechanical Engineering, Applied Physics, or a closely related discipline. · Strong academic background in computational mechanics, fluid dynamics, electromagnetics
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-intensive PhD training programme, supported by the PRIDE funding scheme of the Luxembourg National Research Fund (FNR) and the programme's partner institutions: University of Luxembourg, Luxembourg Institute
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. For more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The selected candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with
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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