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Field
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-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault diagnosis of gas turbines
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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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to achieve scalability in terms of the simulator systems. The work will be done in close collaboration with the physics team to be able to develop optimizations also at the algorithmic level in a co-design way
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power system simulation Energy management system (EMS) or supervisory control algorithm development Hardware-in-the-loop (HIL) platforms (e.g., OPAL-RT, Typhoon HIL) Experience in battery energy storage
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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. The Research Fellow will be employed and based at NTU, and is expected to travel overseas to collaborate with scientific teams at CEA, France. The holder will: Apply advanced machine learning algorithms
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algorithm development Hardware-in-the-loop (HIL) platforms (e.g., OPAL-RT, Typhoon HIL) Experience in battery energy storage systems, power converters, and propulsion drives. Knowledge of control systems
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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Monitoring and Safety Evaluation - Develop algorithms for monitoring human actions during physical task execution using multimodal sensing (vision, pose estimation, and tactile signals). The candidate will
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records. Responsibilities: Assist in the research and development of a large model for 2D navigation. Design, execute, and document experimental evaluations to validate model performance. Design and