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creative and motivated PhD candidate with a strong interest in fluid mechanics and numerical modelling. The position is expected to start in September 2026 and is fully funded for 4 years. Job description
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submanifold (SSM) methods, aiming to further develop concepts in nonlinear model reduction Possible research directions include the development of nonlinear modelling and control approaches for fluid
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-Curie Doctoral Training Network COMBINE brings together 17 academic institutions and 14 industrial partners across Europe.The network addresses key challenges in fluid structure interaction (FSI
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, Medical Informatics, Medical Robotics, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models, Reinforcement Learning, Robotics, Security and Privacy, Smart
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, economics, or a closely related field Excellent academic records and is motivated to produce high-quality, policy-relevant research Have excellent quantitative skills: statistics, optimization, stochastic
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, optimization, and learned surrogate models to design and fabricate efficient, bioinspired robots. Reinforcement Learning and Machine Learning: data-driven methods that let robots learn new skills and
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optimization. Beyond strengthening your scientific and methodological expertise, you will gain experience in clinically grounded research that connects neurorehabilitation, movement science, and data
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interests, the project may include: Development of next-generation focused ultrasound systems fMRI-guided treatment planning and targeting Optimization of targeted drug delivery and therapeutic dosing
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., microbubbles, nanodroplets, liposomes, or polymeric/lipid nanoparticles) Optimization of drug loading, encapsulation efficiency, and release kinetics Physicochemical characterization of carriers (size, zeta
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hidden in conventional measurements. The resulting understanding will guide the synthesis and optimization of semiconductor nanocrystals for applications requiring highly uniform optical properties