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the research include, but are not limited to, field monitoring using sensors (water level recorders, automated weather sensors, water quality sensors) and direct sampling (soil and water samples, auto-samplers
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portable dataloggers (or analogous geophysical sensors) and continuous monitoring workflows. Background in UAS/drone mapping. Numerical modeling skills. Familiarity with coastal water-level/wave datasets and
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resistance, 2) assessing the role of phenotypic plasticity in its competitive success, and 3) developing robust methods for spectral phenotyping using ground, drone, and satellite-based sensors. This position
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, packaging, agricultural systems). Integrate diverse data streams (sensor data, IoT, remote sensing, AI/ML pipelines) into simulation and modeling platforms. Collaborate with GFSI partners and local to global
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. university groups within CMS. Our instrumentation expertise spans detector operations and upgrades (Level‑1 muon trigger, CSC muon system), tracking algorithm R&D, and future HL‑LHC trigger algorithm
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development for data interoperability and system integration. Experience working with digital twin platforms, simulation environments, or real-time sensor data. Prior contributions to research software, open
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying