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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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. A background in forest engineering, ecohydrology, and/or ecology would be ideal but is not required. Experience in numerical modelling, big data analysis, and plant ecological and physiological
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numerical modelling skills (e.g. Python, MATLAB, CFD codes) Personal characteristics Flexible and dependable Collaborative and independent Innovative and open minded Strong analytical skills Emphasis will be
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). ABOUT YOU Applicants should hold a PhD (or equivalent) in synthetic biology, biomaterials, biofabrication, microbiology or biodesign. Being familiar with material-producing non-model microbes and co
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response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design platforms. Furthermore, REUNATECH
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expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently
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(preclinical mouse models) is required Very good knowledge of English, German is an asset (helpful for teaching) Organizational and team skills, flexibility, curiosity, responsible work ethics and a passion for
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motivated and talented doctoral candidate to work on the biophysics of host-pathogen interactions using in vitro model systems mimicking chronic diseases. The project foresees ample collaborative
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security