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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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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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The Australian Institute for Machine Learning (AIML) offers new PhD scholarship opportunities in Industrial AI. These full-time scholarships support students undertaking their PhDs in AI and machine
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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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/scholarships Be inspired, every day Drive your own learning at one of the world’s top 80 universities Take your career in exciting, rewarding directions About the project We’re thrilled to announce a unique
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the world. Ideal applicants will have a solid background in AI, machine learning, control theory or quantitative finance. Applicants with advanced programming skills (Python/C++); and a desire to publish in
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opportunity Experience in machine learning and deep learning Proficiency in one or more high-level programming languages such as Python, Java, or R. Strong commitment to interdisciplinary research and a track
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student