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data from major facilities, including NASA’s Kepler, TESS, and JWST missions; ESA’s Gaia mission; and large-scale spectroscopic surveys such as LAMOST. Experience working with large, complex datasets is
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 4 hours ago
4 Oct 2025 Job Information Organisation/Company AUSTRALIAN NATIONAL UNIVERSITY (ANU) Research Field Biological sciences Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena
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) organization which uses data, by developing best in class computational methods, and applying them to the most relevant scientific problems across all stages of the pipeline. This position is based within
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motivated candidate to carry out bioinformatic analysis on large genomic datasets, with comparisons to associated metadata. This will include development of analysis pipelines and interpretation of whole
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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed
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will research novel methods for effectively working with geometry models of large and complex assemblies in HPC environments to support high-fidelity engineering simulations (e.g. FEA and CFD). This post
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Python – information provided in the CV and/or motivation letter; c) Processing and visualization of large files (NetCDF) – information provided in the CV and/or motivation letter. Contracting requirements
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that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging,” and we love