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skills with experience in cloud data warehouse systems such as Snowflake and data preparation tools like Tableau Prep Experience with predictive modeling, machine learning, AI applications, and advanced
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seminars, MA seminars and/or specialist classes. The selected candidate is expected to teach courses on topics in the field of quantitative finance, machine learning and data science. Courses should be
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AI systems and interpretable machine learning, System integration implementation, Test environment configuration, Validation and stress testing, Deployment and configuration in test environments
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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efficiency, and resource utilization. Strong expertise in machine learning, deep learning, and advanced time series modeling Additional education in economics (e.g., a completed Master’s or postgraduate degree
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contributions as a software engineer for a wide range of projects requiring computing systems design and realization, including machine learning (ML) and artificial intelligence (AI) applications including
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models. The role also requires significant experience in classical machine learning methods such as decision trees, gradient boosting machines, and both shallow and deep learning networks. A demonstrated
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technology in classroom, distance learning and laboratory environments creating and modeling a quality learning environment that supports a diverse student population preparing, distributing and utilizing
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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization