98 data-"https:"-"https:"-"https:"-"https:"-"https:" positions in United Arab Emirates
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Engineering, Computer Science, Computer Engineering, Information Systems, or a closely related field. Technical Expertise in one or more of the following areas: Software architecture and design patterns
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related field. Demonstrated Expertise in one or more of the following areas: Bio and AI: Theoretical and computational biophysics Machine learning and data analysis for biological systems Biomedical imaging
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and cognitive tokens; and data-science of robots and humans including multimodal data acquisition. The real-world in the scope of application spans industrial, agricultural, household, healthcare, urban
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main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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interest may be considered, a priority will be given to those able to relate to one or several of the above topics. For more information on the research directions, applicants are encouraged to check
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cohort and biobank designed to identify novel molecular markers with diagnostic, prognostic, and therapeutic value. This project involves deep profiling of participants through comprehensive data
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface characterization techniques integrating geotechnical and geophysical data. Geohazard mapping and modeling
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cohort and biobank designed to identify novel molecular markers with diagnostic, prognostic, and therapeutic value. This project involves deep profiling of participants through comprehensive data
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to identify novel molecular markers with diagnostic, prognostic, and therapeutic value. This project involves deep profiling of participants through comprehensive data collection on medical history, lifestyle