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Field
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, offering a rich and collaborative research environment. Charles Sturt University is proud to lead the MOSAIC Project – a multi-centre international research initiative aimed at developing and validating a
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at the department) distributed over the employment period. The 4th year is contingent on the qualifications of the candidate and the teaching needs of the department and will be decided by the head of department upon
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for a 4th year, consisting of 25 % career-promoting work (e.g. teaching responsibilities at the department) distributed over the employment period. The 4th year is contingent on the qualifications
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for this position is $65,000/year to $70,000/year. At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all
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are required. UTRGV is a distributed institution, which requires presence at multiple locations throughout the Rio Grande Valley. Other This is a trainee position subject to a specific time as agreed to by
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RESEARCH FELLOW IN ARI(TRANSITION RISKS IN IMPLEMENTING FOREST CARBON INITIATIVES IN SOUTHEAST ASIA)
on the Carbon Governance in Southeast Asia (CGSEA ) project. The position is available on a full-time basis for one year in the first instance, with the possibility of extension dependent on performance and the
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able to successfully perform all required duties. Office/research environment; some travel and weekend work are required. UTRGV is a distributed institution, which requires presence at multiple locations
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. Office/research environment; some travel and weekend work are required. UTRGV is a distributed institution, which requires presence at multiple locations throughout the Rio Grande Valley. Other This is a
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computational tools for quantifying electromagnetic field distributions down to the fundamental atomic scale. The project will build on recent developments in inverse scattering methods, including ptychography
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Engineering, Computer Science, Applied Mathematics or equivalent. Strong background in machine learning, convex optimization, or distributed systems. Prior experience in federated learning, edge computing