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for the 2026 intake. Interview by the respective clusters for shortlisted applicants will be conducted throughout the year. Final Interview by Panel for shortlisted applicants will take place from March to April
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are creating a world-class innovation cluster for the life sciences. For more information about the lab and team, visit www.camunaslab.org and our research profile . 💻 Position 1 – Postdoctoral Fellow in AI
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-ATLAS cluster, composed of three UAE institutions. Candidates must demonstrate experience in experimental high energy physics. The group will be contributing to the Physics Modeling (MC software, MC
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perovskite thin films applications using slot-die coating and other large-area deposition methods. Analyze experimental data to optimize material and device performance, write scientific reports, and publish
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and analysis of big data, especially genetics and genomics datasets. However, we will consider qualified applicants with experience in other related research areas. The successful applicant is expected
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, and to develop therapeutic avenues. The group is particularly interested in understanding how genomic information is used to direct cell-specific gene expression, and to pinpoint gene regulatory defects
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expected to have experience in hydrologic/hydraulic modeling and in one or more of the following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science
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. * Proficiency in R, Python, or related software and programming languages. * Experience with big data, linux systems, and remote computing clusters. * Knowledge of genome-wide approaches and other statistical
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the most suitable data science techniques (e.g., machine learning, cluster analysis) to answer specific research questions based on available data. Running simulation studies and other pre-intervention
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paradigms in primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science