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for the Australian Grains Industry (AAGI) program. The ideal candidate will be an expert in hyperspectral remote sensing with significant experience in data science for geospatial data analytics. This research project
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
: PD&PEWER Level A&B Postdoctoral - Research Fellow _ CSS.pdf This position will provide opportunities to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited
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25 Sep 2025 Job Information Organisation/Company UNIVERSITY OF WESTERN AUSTRALIA Research Field Computer science Mathematics Physics Researcher Profile First Stage Researcher (R1) Country Australia
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classical-quantum algorithms for strongly correlated materials. Key responsibilities include: Research: Establish a research program, collaborate on research projects, seek and manage research funding
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School of Electrical Engineering and Computer Science Full-time (100%), fixed-term position for up to 2 years Base salary will be in the range $82,057.75 - $109,246.18 + 17% Superannuation (Academic
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research