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the direction of A/Prof Claudia Szabo in the School of Computer and Mathematical Sciences at the University of Adelaide. The project is a collaboration with Defence Science and Technology Group, within the Combat
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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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
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problems in the health sciences, including fields such as healthcare informatics, movement and rehabilitation sciences, medical imaging, remote sensing, computer vision, mental health, data fusion
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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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Human-Computer Interaction, Information Science, Computer Science, Design, or related fields - Strong record of published research in HCI, CSCW, DIS, or related venues - Demonstrated ability in
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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the Sterne-Weiler Lab i n Computational Biology / Discovery Oncology and co-mentored by the Frey Lab in Prescient Design (Machine Learning for Drug Discovery). The postdoctoral position is focused
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, including the most recent papers. Preference may be given to candidates with knowledge in machine/deep learning, statistical inference, image analysis, survival analysis, causal inference, and high
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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning