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Field
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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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Overview: Part-time (0.6 FTE), Fixed Term position until 14/05/2027 Salary Academic Level A + 17% Superannuation and Flexible Working Arrangements Based at the Melbourne CBD campus, but may be required to work and/or be based at other campuses of the University About The Position The...
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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Overview: Contribute to world-class research in quantum science at RMIT with an internationally connected research group Two-year fixed-term Research Fellow position (Academic Level B) Based at RMIT’s City Campus in the heart of Melbourne Flexible working arrangements considered, including...
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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, including the role of extraembryonic tissues. Here, we bring together two largely separate fields, evolutionary and developmental biology and bioengineering, to tackle this challenge. By combining
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It