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. Strong programming skills with proficiency in Python and/or C++, and practical experience with deep learning frameworks such as PyTorch or TensorFlow. A track record of high-quality research outputs
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., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted in the Department of Microbiome Dynamics of Prof. Gianni Panagiotou of the Leibniz-HKI in
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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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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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DATASETS AND/OR PEER REVIEWED PUBLICATIONS. - APTITUDE FOR DATA MANAGEMENT AND PROGRAMMING (R AND/OR PYTHON). License/Certification: Knowledge Of: Skill In: Ability To: Preferred Qualifications
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learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
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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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monitoring data and new data collected within the project. Areas and time periods with different abundances of herring will be used to investigate the role of herring in the coastal ecosystem, as prey
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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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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