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Systems and Control division focusing on data-driven control methodologies. About the research project Model-based control is arguably the prime framework to perform certifiably-safe regulation of dynamical
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the potential of heat and flood mitigation strategies using nature-based solutions, in particular green–blue infrastructure, interacting with existing grey infrastructure. The focus is on modelling
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of central venous pressure (CVP) remains challenging in both space and clinical settings. This PhD project aims to develop and validate a non-invasive ultrasound-based method to estimate venous pressure using
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Machine Learning (ML) models, including automated testing, reproducible builds, controlled release strategies, and governance workflow integration. Build and manage containerized infrastructure on AKS
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-phase diagnostics working with catalytic materials such as metal and oxide surfaces, including polycrystalline samples analyzing and interpreting experimental data developing measurement and control
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. • Design, implement, and rigorously evaluate novel AI models and algorithms for multimodal inputs. • Build robust pipelines for data curation, preprocessing, and quality control for noisy, imperfect, and
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welcome: Real-time or streaming pipelines Control / robotics / feedback systems Optical systems / optical tweezers / laser-based manipulation Probabilistic modelling / tracking Software in the loop (SiL
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, operation, and optimization of SPS- and PLC-based control systems for experimental equipment and beamline instrumention Integration of hardware and software interfaces (motors, detectors, sensors, sample
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. Specifically, this project leverages industrial communication networks, distributed controllers, AI models, and state-of-the-art IBR-based infrastructure to optimize EV charging and energy systems. By
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This is a 2-year fixed-term position with the possibility of renewal. This position will be based on the Stanford campus and is a hybrid (working on-site and working from home) subject to operational need