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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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charge which provide an ideal interface between human-built devices (where signals are carried by electrons) and biological systems (where signals are carried by ions). This research will cover theoretical
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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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of eligibility requirements, including English-language proficiency skills, to undertake a PhD in the Faculty of Arts are available at arts.monash.edu/graduate-research/application-process . Applicants should
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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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desirable. The successful applicants must be enrolled on a full-time basis and cannot hold any other scholarship. Application process Please send your CV and a very brief rationale [2-3 paragraphs, maximum
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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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. 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