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, hydrodynamics, machine learning, materials science, statistical physics, strongly correlated electrons, and quantum information. Theorists who wish to be considered for sponsorship as a Klarman Fellowship
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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The appointee will assist the project leader in the research project - “Enabling secure and resilient distribution grid operations through physics-informed topology learning and coordinated network-load
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and microbiological tools, perform field collections of invasive ants and whole nests, and participate in rearing fire ant colonies. Learning Objectives: You will: Gain experience in experimental design
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• PhD degree in STEM. • Proficiency in at least one mainstream deep learning framework (PyTorch or JAX) • Fundamental knowledge of physics, chemistry, and materials science. Research
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progress of work. Provide technical support for other team members through technical advice. Recommend changes to processes or procedures. Must be open to learning and development and be willing to accept
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. Develop AI, machine learning, optimisation, and decision-support algorithms. Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments. Develop
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in our research on understanding the early cognitive and linguistic skills that underly improvements in word learning and other aspects of language development in the first few years of life. This will
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strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people come to do
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uncertainty. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic