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Field
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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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across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both keyword-based search and SPARQL querying. EPRESSO will build on and extend existing frameworks and systems
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that influence global ocean mixing, heat and nutrient distribution, and climate dynamics. Despite their importance, these currents remain poorly understood due to their intermediate scale and intermittent nature
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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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control methodologies) the project will establish a robust framework for coordinating electricity, heat, cooling, and hydrogen flows in distributed energy networks. While the primary focus is on developing
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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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to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed institution, which requires presence at multiple locations throughout
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Job Description Job Alerts Link Apply now Job Title: Research Fellow (Electrolyser systems) Posting Start Date: 27/06/2025 Job Description: Job Description Research Fellow position in the Department
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and weekend work is 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
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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