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for real-world processes and infrastructures (e.g., factories, greenhouses, robotics platforms). Support modeling, simulation, and data-driven optimization using tools such as ROS, Unity, Modelica, Simulink
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experts to integrate LLM-based solutions into predictive maintenance workflows. Explore the use of LLMs for anomaly detection, failure prediction, and optimization of maintenance schedules. Publish high
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composites manufacturing, including resin infusion, automated fiber placement (AFP), additive manufacturing, autoclave and out-of-autoclave curing, and compression molding. Develop, evaluate, and optimize
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, robotics platforms). Support modeling, simulation, and data-driven optimization using tools such as ROS, Unity, Modelica, Simulink, and SCADA platforms. Collaborate with faculty and industrial partners
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methodologies that optimize the design and operation of efficient and sustainable processes. Collaborate with multidisciplinary teams to uncover a large variety of chemical and biochemical processes, and advance
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Hydrometallurgy, with a focus on the development of concepts and methodologies that optimize the design and operation of efficient and sustainable processes. Collaborate with multidisciplinary teams to uncover a
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monitoring and electric vehicle battery tracking. Integrate environmental and energy sensors with real-time data collection and analysis systems. Deploy prototypes in the field and optimize their performance
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trials under different soil and climate conditions to optimize yield and quality of various crops. A good knowledge of biostimulants and organic/inorganic additives and their mode of action to improve crop
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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research team. The successful candidate will work on optimizing methods for nanofibers extraction from various types of biomass, focusing on the development of efficient, sustainable, and scalable processes