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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria - Thesis in natural language processing with machine learning, - mastery of NLP and machine learning methods and tools
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experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects focusing primarily on animal models with
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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machine learning Data analysis and advanced statistics Economic and social transformations related to digitization Experince when it comes to programming (preferably Phyton) and in the use of modern tools
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market using data available on online recruiting platforms, deploying state-of-the-art approaches in Natural Language Processing, Semantic Web, and Agent-based Modeling. For this purpose, an extensive
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during