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This PhD Scholarship is funded by 2022 ARC Discovery Project “Modelling temporal evolution in spatial ecology with dynamical point processes". The world is undergoing rapid environmental change
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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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Location: Turning Point, 110 Church Street, Richmond Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration: There are various scholarships offered by Monash University to support
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not progressed beyond the proof-of-principle. Templating chirality on the surface of stable and common conductors, such as engineered nanoscale three-point chiral kinks on platinum and copper, is not a
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will help increase the material recovery rate in the LIB recycling process and reduce waste and carbon footprint in the battery value chain. By returning waste to battery manufacturing, the project will
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interactions. Machine learning: reinforcement learning, or multi-agent systems. Signal processing: spectrum sensing, localization, or radio environment modelling. Multi-agent systems: distributed intelligence
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other scholarship. Application process Please send your CV and a very brief rationale [2-3 paragraphs, maximum] why you want to join the research project, to the following person in the first instance
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eligibility requirements. Meet Curtin's English language proficiency requirements Application process Step 1: Applicants must find a suitable supervisor who agree to supervise them in their areas of research
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. The underwater acoustic communication technologies will help. The school is focusing on research in AI/machine learning and signal processing which are the research areas in this proposed project. We have
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PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
waveguides and fibres Digital signal processing Design and analysis of photonic systems. Applicants must have a Bachelor/Masters degree (or equivalent) in electrical/electronic engineering, nano