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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
opportunity This role offers an exciting opportunity to develop cutting-edge AI planning and orchestration technologies for Defence logistics, integrating multi-modal agentic AI systems and automated reasoning
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involve the synthesis, physical characterisation and biological assessment of a series of derivatives of the existing candidate formulation. This multi-disciplinary project spans the fields
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and creative team. Strong project management skills with the ability to conduct needs analyses, plan, organize, manage, monitor, complete, evaluate multi-year projects within allocated time and
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of Research Associate/Research Fellow will focus investigating the metabolic basis of agronomic and nutritional traits in Brassica crops through advanced metabolomics and multi-omics integration. The candidate
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researchers at MD Anderson Cancer Center. We are a collaborative team of creative and accomplished researchers, who strive towards discoveries through innovation (www.lulucambronne.org). Our research
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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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libraries such as PyTorch or TensorFlow. Experience with genotype, Raman spectroscopy and multi-modal data integration is highly desirable. Experience with population-level administrative databases and data
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career. The BC Centre for Vulvar Health and Dr. Katrina Bouchard in the UBC Department of Obstetrics and Gynaecology are seeking a full-time (1.0 FTE) Postdoctoral Research Fellow to advance
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high-dimensional omics data, identifying predictive biomarkers, and understanding cancer heterogeneity. Projects include modeling of bulk and single-cell transcriptomic profiles, integrative multi-omics
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services are regularly implemented in a sector specific manner, meaning research is needed to better understand where multi-sector and cascading risks can be identified, managed, and modelled. This research