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in the fermentation process. Participate in the development of a comprehensive digital twin for monitoring, prediction, and control of fermentation processes. Work cooperatively and constructively
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, physics of fluids, flow control... The main objective is to develop numerical tools to efficiently predict and to deeply understand flows in more and more physically and geometrically complex configurations
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apply. Understand ML/DL frameworks such as TensorFlow and PyTorch. Familiar with version control systems such as Github, Git, etc. TOEFL, IETLS or other certificates that can prove your English level
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
impacts. The candidate will develop and validate computational models capable of predicting penetration, damage evolution, and energy absorption, while exploring novel lightweight material configurations
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the business landscape and service market. In the future market, also referred to as Market 4.0, ecosystems are predicted to be in a dominant position, and various forms of cooperation and joint operation will
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential
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to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic
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candidate will be involved in the development of model predictive control to operate the energy system and IT load largely self-sufficiently under varying weather conditions, with particular focus on the role
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lifespan of geothermal systems depend on the subsurface distribution of heat, fluid flow pathways, and recharge mechanisms and fluid chemical composition. These factors control the precipitation, dissolution
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predictive maintenance of ships and maritime systems. Modern vessels generate large amounts of heterogeneous operational data from sensors, machinery, control systems, maintenance records, and other sources