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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
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Faculty Positions in AI for Theoretical and Physics-Informed Modelling (Full Rank) Position Overview School: School of Mathematics and Physics Department: Department of Physics Position: Faculty
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Overview Deliver core courses (e.g., Mathematical Modeling, Data
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entering a new and exciting phase of its development as part of its strategic priorities for the next ten years. Adopting a new higher education model based on the concept of Syntegrative Education (SE
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experience and solid expertise in one or more of the following areas: Advanced Process Control and Optimization Digital Twin and Modeling & Simulation Predictive Maintenance and Fault Diagnosis Industrial IoT
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Assistant Professor/Associate Professor/Senior Associate Professor/ Professor in Applied Mathematics
in education models, and is now transitioning from a collection of distinct approaches toward a unified, integrated Academy model – a syntegrative ecosystem designed to break down silos, enhance
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phase of development. XJTLU continues to innovate in education models, and is now transitioning from a collection of distinct approaches toward a unified, integrated Academy model – a syntegrative
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disease models, efficacy & toxicity testing. Drug Delivery & Novel Formulation Research – nano and micro carriers, controlled release, targeted delivery. Pharmacometrics – population PK/PD, systems
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include but are not limited to statistics and probability, stochastic modeling, scientific computing, mathematical optimization and control, numerical analysis, and relevant adjacent fields. Candidates with
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Xi'an Jiaotong - Liverpool University – XJTLU Entrepreneur College (Taicang Campus) - School of Robotics Academic Discipline: Electrical and Electronics, Mechatronics, Intelligent Controls, Robotics
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis