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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure, improved mechanical and corrosion properties. Research stays are planned
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intelligence. In these research areas we focus on 1) foundations, 2) system design, and 3) applications. IDLab collaborates with many universities and research centres worldwide and jointly develops advanced
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of Antwerp. See also: https://remotesensing.vito.be/news/sspirit-tackling-plastic-pollution The main task of the KU Leuven PhD project will be the further development and validation of a two versatile
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research, including the development or integration of algorithms, decision support tools, predictive models, artificial intelligence, visualization and analysis of health data, as well as the use
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sensitivity analysis, impact of the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working
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projects in the group as well as with other activities at the department. The main supervisor will be Assoc. Prof. Francesco Da Ros, DTU Electro. Responsibilities and qualifications The exponential surge in
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. The activities within the project will benefit from synergies with other projects in the group as well as with other activities at the department. The main supervisor will be Assoc. Prof. Francesco Da Ros, DTU
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matter physics, and quantum computing. You will dive deep into the realms of theoretical and computational physics with a focus on the development of algorithms and large-scale numerical simulations. Your
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent