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Field
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to cutting-edge research at the intersection of artificial intelligence and life sciences. Responsibilities: Develop and implement new methods for the analysis of biomedical images, with a focus on brain
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for simulations, we aim to explore solution strategies to calculate the amount of water given meteorological data and map data. Here, in addition to traditional discretization methods such as finite elements and
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. Qualifications • PhD Degree in a relevant discipline, e.g. electrical / electronic engineering, computer engineering, computer science, etc. • Full experience in computer graphics and robotics area
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computer vision and machine learning research group in Australia -- and contribute to world-leading research projects at the CommBank Centre for Foundational AI This postdoctoral research position is part of
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of the following areas will be a merit. Advanced AI methods development in Python or any other relevant programming language Computer Vision and image analysis Handling of large dataset Qualifications Applicants
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methods Experience analysing RNAseq and/or high throughput imaging data Experience handling human post-mortem tissue Experience writing/using computer code Experience supervising postgraduate students and
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how people estimate quantities. The project combines theoretical modeling with behavioral experiments to advance our understanding of the cognitive processes that connect our sense of magnitude with
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capturing real human mental performance, constrained by limited time and subject to systematic errors, requires transforming rational models into process models that approximate probabilistic calculations in
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
for robotics. This project is a collaboration with the Australian Defence Science and Technology. It aims to develop computational representations and methods for efficient sequential decision-making under
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approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own