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The Statistics (STAT) program in the Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah University of Science and Technology
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, microplastics; by means of numerical and experimental approaches including high-performance computing, data science methods via machine learning/AI and digital twins, enhancement, development and of application
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) Application Deadline 19 Sep 2026 - 21:00 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a talented and responsible
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initiatives. Working with the Hastings Advisory Committee to further develop the Initiative. Supporting the Initiative’s Director in administrating the Initiative Developing the Scholar’s own research program
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cybersecurity research conducted within the Department of Computer Science. The researcher will work closely with the Principal Investigator and undergraduate research students, with responsibilities including
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Engineering Computer Sc. Demonstrated evidence of developing Machine Learning and Optimization models for Air Traffic problems. Two years of working experience in Machine Learning and Data Science Strong
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Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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Link https://www.ubjobs.buffalo.edu/postings/64290 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information Position
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning