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to a positive performance evaluation. Keywords: Atomic physics, Simulations, Rydberg Atoms, Many-body quantum physics, Quantum information theory, quantum computing, artificial intelligence, machine
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data integration data quality data analytics programming languages (SQL, Python, Java) Complementary IT skills: machine learning image recognition sensor technologies robotic technologies
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; mechanism design and institutional design; cooperative AI and multi-agent systems; the study and development of large language models; cross-cultural psychology and large-scale behavioral data; philosophy
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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and green energy applications, big data and AI in smart sensor technology, and quantitative and systems biology. These efforts are supported by infrastructural and internationalisation initiatives
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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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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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including
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interdisciplinary team spanning statistics, machine learning, genetics, and population health. You will work closely with collaborators at the Nuffield Department of Population Health (NDPH), the Big Data Institute