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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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into the network led in 2017 to the detection of a neutron star binary merger that could be followed in electromagnetic signals, representing the beginning of multi-messenger astronomy. At the moment, LIGO and Virgo
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, Physics , quant-ph , Quantum Science + Quantum Information Science + Quantum Optics + Theoretical Physics , Quantum Sensors , Theoretical Particle Physics , Theoretical Physics, HEP-Phenomenology (hep-ph
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networks and advanced research infrastructure, SII-Lab , makes us a competitive international actor. About the research project The research will be conducted on enabling the manufacturing industry
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, School of Physical Sciences, and the University of Lincoln’s research themes of Sustainability and Net Zero. You will have, or will soon obtain, a PhD in chemistry. Each of the positions will require
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funding, Profs. Himanshu Gupta and CR Ramakrishnan conduct research in the general area of quantum networks, quantum sensor networks, and distributed quantum computing. The center includes other quantum
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trophic (phytoplankton growth and loss) variables of the Thau lagoon and the Mediterranean Sea (Station 00SETE) in an innovative way using in situ data from high-frequency automated sensors; 2) linking
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Education and Experience: Appropriate PhD in a related field. Preferred Qualifications: Experience with machine learning and deep neural network techniques. Experience with wearable and sensors placed in