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Field
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, and reproducible code development skills; distributed or parallel computing is a plus. Experience designing and executing field experiments in urban or environmental settings, with willingness to
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Administrator (Senior Research Fellow) to oversee and optimize our High-Performance Computing (HPC) and distributed AI infrastructure. In this role, you will lead the architectural design and
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computing, compiler engineering, distributed systems, or DevOps are encouraged to apply, as well as others with related experience that can be applied to further the objectives of this project. The
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learning approaches, we are recruiting a team of outstanding researchers in cloud compute architecture design and event-triggered computing to work with us on realising SONNETS' ambitious objectives
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innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and photonics. Many of the
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Artificial Intelligence, Software Engineering, Edge Computing, and/or Cybersecurity. Hands-on experience with modern edge AI technologies/frameworks, such as small language models (SLM), distributed large
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assigned. Supervision Received General supervision from immediate faculty supervisor. Supervision Given None. Required Education Doctoral degree in Biology, Statistics, Computing, Human Genetics or
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systems, including embedded and resource constrained systems with various heterogenous computing topologies Additional responsibilities include Assisting with development of GUI software tools to
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from sparse spatial inputs Integration of foundation or multimodal models into geospatial production workflows Minimum Qualifications: • Ph.D. in Geomatics, Spatial Computing, Computer Science, or a
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 7 hours ago
-assisted subsurface characterization and monitoring Distributed Acoustic Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion