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Field
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
or a related discipline, with an emphasis in numerical computation intermediate to advanced skills in large data analysis and manipulation, including programming in different languages including Matlab
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to implement data analytics workflows and algorithms using scientific programming languages (preferably Python but others like R, Julia or Rust can also do). Ability to cooperate with a multidisciplinary
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: · MATLAB · Python · ROS · Computer vision and/or YOLO · Pytorch and/or Tensor Flow · LiDAR · GIS · GNSS receivers Minimum Qualifications: Doctoral degree in Mechanical Engineering, Electrical Engineering, or
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publication record in reputable peer-reviewed journals is highly desirable. Proficiency in programming, particularly in R, Python, or other relevant languages, is required. Qualified candidates should also meet
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making a difference to people's lives. We believe that inspiring our people to do outstanding things at Durham enables Durham people to do outstanding things in the world. Being a part of Durham is about
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information for ocean health, sustainable blue economy, and coastal climate risks, downstreaming the data flow from climate ensembles to coastal areas at different spatial resolutions and for selected areas, in
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(or a related field), is proficient in both mechanistic modeling and ML frameworks (e.g., TensorFlow, PyTorch), and has strong programming skills (Python/MATLAB/C++). Join us to tackle sustainability
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for loading LLMs; Experience with Cloud Platforms (Google Cloud to be preferred); Advanced Python programming, including developing course material and exercises for chemical engineering subjects; Experience
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imaging, especially in vivo imaging Coding experience in Matlab, Python or R Record of first-author publications or preprints Success in competitive funding schemes/awards Experience with inflammatory
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Sensing (DAS) data from different locations in the Sea of Marmara region. The main purposes are (i) to compare detection thresholds between different types of instrumentation, (ii) to monitor seismic