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future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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and coordinate with growers, collaborators, and research staff across multiple locations. - Maintain research records and assist with data analysis and reporting. Minimum Qualifications: - Ph.D. in
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alignment with the project’s coding framework. Conduct comprehensive literature reviews on topics such as refugee student experiences, culturally responsive pedagogy, multilingual language learners (MLLs) and
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are welcome, but these should be aligned with UniSQ's flagship areas . Students applying for a scholarship should discuss the flagship area most closely aligned to their proposed research with their potential
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are welcome, but these should be aligned with UniSQ Flagship Areas . Students applying for a scholarship should discuss with their potential supervisor the Flagship Area most closely aligned to their proposed
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, safety, health and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Deliver ORNL’s mission by aligning behaviors, priorities, and
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experience and how this aligns with the PhD project) evidence of your enthusiasm for, and experience in, multidisciplinary research evidence of your ability to work independently and as part of a team to
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aims to develop a novel theoretical framework for nonlinear and robust control of dynamical systems from a phase perspective. You will have the opportunity to freely explore multiple research directions
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(Agarwal et al., Quant. Sci. Technol. 2024), so that for any verification approach to be useful it needs to go beyond widely employed Markovianity assumptions. This work, directly aligning with NQTP Missions