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Field
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undertake research in quantum algorithms, error correction, and fault-tolerant quantum computing architectures, with applications to decarbonisation. Based within the Queensland Quantum Decarbonisation
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aligned with the aims of the Terascale project. Develop and apply suitable methodologies for research for AI for science and catalyst discovery. Design, implement, and evaluate novel models, algorithms
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-structures. Develop numerical algorithms and computer codes that can be used to model nonlinear optical effects pertaining to light-matter interaction. Validate the theoretical models and software tools by
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2029. This role is eligible for hybrid working with a minimum of 60% on site. If you need any reasonable adjustments to apply, contact About you You must have: A PhD (awarded or near completion) in
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system model new module integration, scenario simulations, and prognostics analyses Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: A
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estimation algorithms that operate reliably in challenging environments (e.g., low texture, illumination variation, dynamic scenes, or sensor degradation). The research will integrate model-based estimation
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technologies across a wide range of electrical machine topologies and applications. This includes research into permanent magnet and non-permanent magnet machine drives, high- performance control algorithms
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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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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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algorithms for: Fine-grained video understanding Concept learning Temporal reasoning Vision-Language Models (VLMs) Multimodal representation learning Self-supervised and weakly supervised learning Fine-tune