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algorithms into practical biopharmaceutical applications while remaining grounded in an academic environment that values rigour and publication. You'll be part of a multidisciplinary team, supported by
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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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to the present and modelling towards the future. Research within BEES is clustered within four general thematic areas: Ecology and Evolutionary Biology; Climate Science; Environmental Change, Sustainability and
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
the motivation of gender-fair insurance pricing. As appropriate, the Postdoctoral Fellow will also collaborate with other researchers to develop scalable computational algorithms and translate the proposed
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queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently
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systems, using techniques from areas such as functional analysis, operator theory, ergodic theory, differential geometry, data science, and/or machine learning. Implement algorithms that efficiently analyse
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
strategies, signal processing algorithms and simulation platforms for goal-oriented semantic wireless systems. Drive a high-impact ARC Discovery Project aimed at advancing sovereign telecommunications
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and artificial intelligence. Reporting to the Technical Lead of CardiacAI , the role will contribute to the development, validation and evaluation of predictive algorithms, clinical decision-support
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at its best moves between fundamental work — in learning theory, algorithms, and representation — and applied work that puts it to use in the world. We welcome researchers who thrive in either or both. We