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management, analytics, machine learning, and artificial intelligence. Its objective is to contribute to the advancement of scientific knowledge in the field of data-centric systems by addressing the challenges
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boosting, and related machine-learning approaches Evaluation of model generalizability using train/test splits, leave-one-cohort-out validation, and leave-one-platform-out validation Assessment of predictive
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error correction, text generation, computational morphology, syntactic parsing, dialect and non-standard language modeling, pedagogical applications, machine translation, and text analytics. Since its
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package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https
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for Languages (CEFR). Knowledge of Python programming. Knowledge of JavaScript. Practical experience in artificial intelligence, machine learning or deep learning, in academic or business environments. Training
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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advance machine learning and natural language processing (NLP) methods to analyse complex information manipulation, legal responses, and opposition actors across critical domains such as election integrity
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that support advanced machine learning, reinforcement learning, and foundation model research across large-scale clinical environments. What You'll Do: Design, build, and optimize distributed data processing