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studies. Develop, maintain, and optimize computational pipelines for the integration and analysis of multimodal molecular and clinical datasets, including data generated from patient tissue specimens and
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louvre panels that achieve objectives like preventing wind-driven raindrops from entering, optimizing natural ventilation with minimum flow resistance, enhancing daylighting which may not be achievable
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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the mathematical relationships, trade-offs, and impossibility results connecting different fairness definitions. Intersectional fairness: exploring fairness guarantees for combinations of multiple protected
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 19 days ago
clinicians, academics and health care leaders of the future who work in partnership with their patients and colleagues to provide optimal care for individuals while strengthening the system and society they
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geometries, and optimization of surface and volumetric meshes. The fellow will also develop and evaluate multiphysics computational models coupling microwave electromagnetic energy deposition with bioheat
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data collected longitudinally across the post-infection study time course to optimize machine learning methods that predict disease outcomes and identify host factors and interactions that most heavily
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• A strong foundation in mathematics, particularly in probability and optimization. • A PhD degree (or close to PhD completion) in engineering or management science degree, related
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modelling and optimization techniques, this research aims to identify optimum design and operational strategies that enhance system flexibility, improve resilience to variable supply and demand, and minimize
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-resistance phenotypes. You will gain experience applying AI, genomics, and bioinformatics, from foundational AI/ML concepts to hands-on application and optimization of DNA and protein language models