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-performance chain; therefore, enabling stability and control of novel manufacturing processes as well as achieving desired properties within materials science and engineering. Use-cases will be defined within
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operation, and system reliability. As part of this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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(MHH). The project is funded by the Wissenschaftsräume programme of the state of Lower Saxony and the Volkswagen Foundation. Your tasks will include: Design the physical intervention component of the app
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disciplines. Besides the funding the support entails an interdisciplinary training programme, which aims at encouraging debates on the roles of science and belief or society and the Church. Financial support
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in inorganic synthesis and/or Schlenk-line preparations Experience with scientific programming (e.g., MATLAB, LabView) is advantageous Experience performing quantum chemistry computations (e.g., DFT
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collaboration Comprehensive onboarding and open knowledge exchange within the team Extensive training and further education programs at our UKE Academy for Education and Careers Opportunities to contribute
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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent
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consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods into the open data AgiMo Digital Twin, (4
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consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods into the open data AgiMo Digital Twin, (4