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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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This PhD research scholarship (Learning Lessons from Drug Resistance to Tackle Herbicide Resistance) is funded by the Australian Research Council to support a full-time PhD student to undertake
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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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the academic staff at SIT. We are looking for PhD students to work on projects on stochastic optimisation algorithms for hyper-parameter tuning in Machine learning. The successful candidate will explore
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agents. Your research will explore how reinforcement learning, multi-agent cooperation and generative worldmodels can deliver adaptive strategies that thrive amid volatile, multi-asset markets, micro
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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging