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on efficient and scalable training, model interpretability and explainability, and reproducibility in high-dimensional machine learning frameworks. The project aims to advance the research frontier in
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about advancing Machine Learning by integrating
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modeling and skilled in numerical simulations, will design a mesoscopic radiative model aimed at overcoming the CFL constraint and exploring machine learning methods and neural operators to address
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 1 day ago
within six months of the appointment commencement) and experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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the foundational mathematics and programming skills necessary for creating basic neural networks and deep learning models from the ground up. Additionally, it is designed for those keen on comprehending
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quantitative discipline or equivalent experience. · Experience applying statistical or machine learning methods in real-world contexts. · Proficiency in Python and/or R for data analysis and modelling. · Strong