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consisting of PAT and mechanistic / data driven modelling allowing process control. Steps to be taken will be: Developing a process applicable PAT method (single / multisensoric) for AAV / LNP / VLP detection
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to an unprecedented extent. The main topics include the description and modelling of wind turbulence, the analysis of interactions of turbulent atmospheric wind flow and wind energy systems, as well as control of wind
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. The acronym CAUSE stands for Concepts and Algorithms for - and Usage of - Self-Explaining Digitally Controlled Systems. Digitally controlled systems are ubiquitous in our everyday lives, from transportation
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or discrete probabilistic structures is beneficial but not required. What we offer: WIAS Berlin is a premier research institution known for its strength in optimization, optimal control, dynamical systems, and
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree
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sufficient command of the relevant ancient languages for their dissertation project. During the course, doctoral researchers are required to complete the curriculum of the doctoral program (cf. academic rules
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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++ is expected. A good command of English is essential, both as the local working language and because of international collaborations. We look for a competent personality with initiative and commitment
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grades in all components good command of English aptitude for academic work Organizational and communication skills as well as the ability to work in a team, flexibility and initiative Our offer
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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