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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g
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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 mobility of IoT devices. This thesis proposes leveraging intelligent softwarization—using Machine Learning (ML), Software-Defined Networking (SDN), and Network Function Virtualization (NFV
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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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cleaning and quality control, supervised and unsupervised machine learning, parametric and nonparametric statistical methods, deploying production models, and assisting with the communication of scientific
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. Experience with Python programming. Familiarity with machine learning methods. Strong communication skills and ability to work collaboratively across theory and experiment. Desired Qualifications PhD in
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About the Opportunity JOB SUMMARY The Sr Machine Learning (ML) Engineer applies expertise in deploying and scaling AI pipelines across at least one major cloud platform (AWS, GCP, or Azure
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, and machine learning methods. Using regression analysis and vector autoregression (VAR) models, the study examines the relationship between macroeconomic variables and the performance of various