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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy
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live in. Your role To this end, one PhD student will be hired to perform research in the domain of quantum computing applied to optimization problems with possible topics covering: Variational quantum
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Research, has formed a dedicated doctoral training unit (DTU) on “Forest function under stress” (FORFUS). It consists of 4 inter-linked research clusters, focusing on below-ground processes, tree and canopy
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Do you want to contribute to the future sustainable use of forests? Apply to join the WIFORCE Research School at the University of Agricultural Sciences (SLU) in Sweden! The recruited PhD students
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a dedicated doctoral training unit (DTU) on “Forest function under stress” (FORFUS). It consists of 4 inter-linked research clusters, focusing on below-ground processes, tree and canopy processes
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application is 1st of December 2025. Project description The project is focused on preparation of various carbon materials, including graphene related materials, optimized for application in sorption/separation
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development of implements for managing main and support crops in the field, tested on stationary gantry robots and mobile platforms. Work includes lightweight, structurally optimized mechanical design, sensor
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of biodegra
Cutting-edge Research for a Changing World PhD position – Ad Print4Life DC11 Reference code: 50156132_2 – 2025/MO 3 Commencement date: March 1st, 2026 Work location: Geesthacht Application deadline
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on enzyme stability under process-relevant conditions (e.g. air–liquid and gas–liquid interfaces), redox enzyme applications, and integration into continuous and multiphase systems. The work will bridge
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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