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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 18 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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and next-generation sequencing is highly preferred · Experience with single-cell data analysis · Proficient in Linux/Unix-based high-performance computing ( HPC ) environments and job
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genomics, statistical genetics or genomic data analysis. · Strong quantitative and programming skills, with experience in R, Python or comparable scientific computing environments. · A record of rigorous
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DECIDE research team. DECIDE conducts cutting-edge research on Distributed and Embodied Computing in Dynamic Environments. The team works to realize a vision where humans and robots cooperate
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-time inference within rendering pipelines Minimum Qualifications: • Ph.D. in Computer Science, Visual Computing, Interactive Media, or related field • Strong C++ and/or C# programming expertise
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researchers at the College of Computing and Data Science (CCDS) at NTU. Technical skills, natural curiosity, versatility, and enthusiasm are highly desired traits that will enable success in this fast
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, contrastive learning, or representation learning approaches Experience with high-performance computing, cloud platforms, or distributed training Prior experience working in a collaborative biomedical
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will develop novel theoretical frameworks and signal processing techniques (traditional and/or AI based) for Near-Field ISAC in distributed cell-free 6G networks. The research will investigate the
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analyses using GIS-linked eRegistry data. Develop reproducible R-based analysis pipelines, including support for DataSHIELD or other privacy-preserving/distributed analyses. Contribute to data harmonization
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Pleistocene–Holocene transition. Range expansions and demographic change leave signatures in the genome, including changes in genetic diversity and in how genetic variation is distributed within and