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frameworks for distinguishing closely related quantum processes that arise in light-matter interactions, and to use these frameworks to extract fine spectral or temporal information from weak or structured
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focus on quantum channel discrimination for high-resolution spectroscopy and AC field sensing. The project aims to develop theoretical frameworks for distinguishing closely related quantum processes
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with a new cutting-edge quantitative-trading company to push the frontiers of AI-aided decision-making in quantitative trading processes. As a PhD candidate you will: Design next-generation trading
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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study flow dynamics relevant to reactor design using optical diagnosing methods, followed by image processing, which may include machine learning-based techniques. This suits Mechanical Engineering
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the following skills and qualifications (tailored to the specific project): Driven individuals who want to be a part of a world class team Some familiarity in healthcare or engineering/image based analysis
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Status: Closed Applications open: 19/09/2025 Applications close: 19/10/2025 View printable version [.pdf] About this scholarship Description/Applicant information Forrest Research Foundation PhD
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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, the aim of this project is to develop and validate an experimental paradigm that can describe the dynamic processes underlying C2 agility and to characterise the situational factors by which C2 agility can
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to apply: Seismic wave propagation theory; seismic data acquisition and processing; signal processing, seismic imaging, geophysical inversion, scientific programming, reservoir modelling. Previous research