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Field
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, or network-based, Bayesian or matrix factorization methods for multi-omics integration Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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of the research include: (1) Designing and executing methods to integrate data from different sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches
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, as well as machine learning techniques. Experience to adapt existing methodology to new situations. Thorough skills in analysis and consultation. Demonstrated experience with data analysis, computer
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as opportunities allow. We are looking for a post-doctoral researcher to join a research project which brings the Active Inference perspective to the study of human computer interaction. This requires
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the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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software, technology, and relevant computer applications. Communication: Strong and clear written and verbal communication skills for interacting with colleagues and stakeholders. Department Specific
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is