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Field
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design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed-term position of three years, including
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be studied rigorously. Applicants may have a background in natural language processing, computational linguistics, computer science, machine learning, artificial intelligence, data science, digital
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Job Requirements: PhD in geodesy or related field At least 3 years of relevant experiences in GNSS data processing Strong computation skills with experience using MATLAB and/or Python Excellent
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genetic manipulations to study sensory processing and motor sequence generation. Analyse experimental data, maintain reproducible research records and interpret findings in relation to neural circuit
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‑enabled data analysis, including collaboration on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and
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seismic datasets, including signal processing, imaging, inversion, or source characterization, is highly desirable. Proficiency in Python, C++, or Julia, with strong numerical modeling, data analysis
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for a Research Fellow (RF) to join EOS. The successful candidate will work under the supervision of Asst Prof Lujia FENG and focus on GNSS data processing and analysis for tectonic deformation studies
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external partners. Job Requirements: PhD in Civil/Structural Engineering, Geophysics, Seismology, Engineering Mechanics, Electrical/Computer Engineering, Applied Physics, Optical Engineering, Computer/Data
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 13 hours ago
-assisted subsurface characterization and monitoring Distributed Acoustic Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion
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longitudinal data on PhD recipients with complementary sources such as publication and patent data, and aims to enrich these with other sources on individual paths and preferences. The PhD candidate will