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Field
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. Desirable Criteria: Interest in application of research to Defence and Intelligence domains, and collaboration with industry Familiarity with formal mathematical proofs and analytical methods, or numerical
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opportunities via the academic promotions process. About You Completion or near completion of a PhD in human-centred AI, human factors/cognitive psychology for decision support, or closely related area. Openness
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Academic Level B: Completion of a PhD in the field of Computer Science/Artificial Intelligence. Software engineering expertise, including design and implementation of AI-based models (machine learning, deep
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quantitative methods. Strong written and verbal communication skills, including academic writing. Proficiency in data analysis software (e.g., NVivo, SPSS, SAS, Stata). Understanding of ethical and governance
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manufacturing principles. Experience with machine learning methods and integration into hybrid modelling systems Demonstrated ability to clearly communicate research concepts and results in high-quality journal
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and performance of existing methods by generating and pursuing novel ideas and solutions to scientific research problems. Evidence of advanced programming skills in languages and statistical software
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field (e.g. physics, chemical engineering) Demonstrated experience in developing computational methods and workflows for chemical problems and experience using simulation software Demonstrated subject
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anatomy and lignocellulosic composition, develop high-throughput screening methods, and identify genomic regions linked to key traits. Your work will directly contribute to breeding resilient, high-value
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mentoring research assistants and postgraduate students, and providing guidance on research methods and project execution. To be successful, you’ll have: PhD in in international business, sustainability
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for Australia. Key responsibilities will include: Research: Extensive experience in power systems stability assessment when only black box models are available. Develop new data-driven impedance modelling method