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Field
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Responsibilities: Conduct programming and software development for data management. Design and implement machine learning models for optimizing data management. Conduct experiments and evaluations of the designed
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Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning frameworks. Proficiency in
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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of machine learning, simulation-driven testing, and iterative calibration based on real-world datasets. Contribute to scholarly publications, technical documentation, and progress reports required by funding
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. For all posts, applicants should also: (a) be proficient with machine learning toolsets such as CUDA and PyTorch; (b) have experience with game development platforms such as Unity or Unreal Engine; (c
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will join a team of probabilistic modellers and machine learning researchers developing new collaborative AI principles and methods. This is an exciting topic which inspires new problems in fundamental
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and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace
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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities