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lensing or RF acceleration. Strong data analysis skills. Fluent oral and written communication skills in English. Desired qualifications: Work experience from particle-accelerator laboratories. Experience
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: Development and calibration of numerical models of pilot networks to explore the use of multiple monitored parameters (pressure, flow rate, basic and advanced water quality parameters) for anomaly detection
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of relevant experience in model development and the design of numerical experiments, development of novel convection schemes (e.g., Develop new functions for both deep and shallow convection schemes
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, microbiological, or molecular ’omics preferred. Familiarity with methods for high-throughput sequence analysis, ideally microbial community metagenomics and/or microbial genetics. Excellent record of scientific
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“Methods for Wideband Active Acoustic Monitoring”. This is a great opportunity to work with acoustics and signal analysis in the scientific forefront, with short distance to industrial applications and in
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biology, mathematical and computational finance, numerical analysis, machine learning and data science or the Oxford Centre for Industrial and Applied Mathematics (OCIAM). The successful candidates will be
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, Statistics, Physics, or a related quantitative discipline. You have demonstrated experience in mathematical modelling and or statistical analysis. You show a strong aptitude for research, supported by a sound
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experience to handle large-scale numerical modeling and analysis, and management of computational workflows Strong publication record relative to career stage Strong skills in scientific analysis
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satisfactorily complete suitability and reliability screening and analysis. The participant must maintain the standards prescribed for the Army Biological Surety Program, which may include federal drug screening
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to research to operation project funded by the National Oceanic and Atmospheric Administration (NOAA) under the National Weather Service (NWS) initiative. The Postdoctoral Research Fellow designs analysis