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collection, and applied mathematics. Our goal is to make optimal use of past climatic information to better understand Earth’s climate response to various kinds of forcing, focusing on abrupt climate
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recommendations are shared with our Partnership and Advisory Group who work with us to ensure that our understanding is optimal and our dissemination approaches effective, as well as informing any new initiatives
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will then analyse complex patterns of data and derive an optimal set of items to form a smart self-report instrument. This two-year project is fully funded by the Hearing Industry Research Consortium
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vulnerable to hardware-level threats, including side-channel attacks, fault injections, etc., particularly when optimized for performance. This Research Fellow position focuses on AI security in the context
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development, paleo-climate data collection, and applied mathematics. Our goal is to make optimal use of past climatic information to better understand Earth’s climate response to various kinds of forcing
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML