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expertise, the work may involve wireless communications, multi-robot coordination, trajectory planning, artificial intelligence, optimization, and the experimental validation of communication-aware robotic
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, optimization, and the experimental validation of communication-aware robotic systems. Position Requirements: Ph.D. in Robotics, Electrical Engineering, Telecommunications, Computer Engineering, Control
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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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. The project focuses on developing sustainable and optimized processes for the purification of industrial acidic and complex aqueous media through the selective removal and recovery of impurities to enable
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experimentation, modeling, simulation, and optimization of chemical processes. The successful candidate will work on the development of advanced process models combining experimental investigations with
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a highly motivated Postdoctoral Researcher in Process/Chemical Engineering to contribute to advanced research in experimentation, modeling, simulation, and optimization of chemical processes
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of acidic and aqueous media, impurity separation, and materials characterization to join a cutting-edge research team. The project focuses on developing sustainable and optimized processes
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analysis (TEA). The successful candidate will: Develop innovative strategies for transforming industrial byproducts into high-performance porous adsorbent materials. Design and optimize structuring and
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computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. The ideal candidate should have a strong background in artificial intelligence and
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will focus on the formulation and synthesis of advanced synthetic polymers with optimized architectures and tailored functionalities for industrial applications, such as wastewater treatment, fertilizer