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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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experiments in particle physics. Join Us! We are seeking a PhD candidate under the supervision of Jordy de Vries who focusses on precision calculation for low energy searches for beyond the Standard Model
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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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-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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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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models of the various equipment/tools of an agricultural machine, and virtualisation of their use in the field - Determination of the control logic to be implemented on the platform (proportional valve
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flexible compensation packages to attract and retain top talent. Benefits Rutgers provides a comprehensive benefits package to eligible employees. The specific benefits vary based on the position and may
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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
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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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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness