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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 2 months ago
years. This role will develop and apply new machine-learning based approaches for extremely precise radial velocity studies and exoplanet spectroscopy with the NEID, HPF, and MARVEL facilities, and pursue
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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the Department of Physics. Machine learning has made enormous progress during recent years, entering almost all spheres of technology, economy and our everyday life. Machines perform comparably to, or even surpass
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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11.12.2025 Application deadline: 15.02.2026 The Faculty of Science at Tübingen University invites applications for a W3-Professorship in Machine Learning in Physics at the Department of Physics (m/f
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these plants. The post-doc is expected to build upon existing in-house tools and, where applicable, enhance them by means of AI (machine learning) and data-driven methods. These models are aimed to support
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computational astrophysics, including experience in numerical methods and machine learning. Research experience at the postdoctoral level, with an established record of publications and impact appropriate
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Development of innovative experimental model systems for mechanistic investigation and translational validation of microbiome-mediated processes Advanced AI and machine learning frameworks for integrative multi
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. · Strong background in machine learning/AI and hands-on experience with large, heterogeneous datasets. · Practical experience with computer vision and/or spatio-temporal modeling (object detection