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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling
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for AI and Machine Learning included as well as industrial statistics), which will complement our current research portfolio (see https://stat.kaust.edu.sa) and have a research profile that can potentially
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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data, log-trace data from learning platforms, and panel data. Relevant areas of expertise include longitudinal data analysis, psychometrics, learning analytics, and machine learning. We are particularly
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data (HRMS) used for non-target analysis. The projects aims to develop a combination of supervised and unsupervise machine learning stragaties for pinpointing chemicals that have high toxicity
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for machine-brain interface, tissue repair, and disease diagnosis, monitoring and treatment, are especially encouraged to apply. The appointee is expected to conduct world-class research and to teach
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to apply Website https://www.academictransfer.com/en/jobs/359291/postdoc-in-machine-learning-and… Requirements Specific Requirements We will base our selection on the following components: a PhD degree in an
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analysis and/or advanced algebra or algebraic topology. Knowledge and experience of machine learning. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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» Computer engineeringEducation LevelPhD or equivalent Research FieldEngineering » Communication engineeringEducation LevelPhD or equivalent Skills/Qualifications PhD (or equivalent) in computer and