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Field
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with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers. The successful candidate will lead development of variable importance measures – including
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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dataset, GroupLens Research. grouplens.org [8] Statlog German Credit Data, UCI Machine Learning Repository. archive.ics.uci.edu [9] ERASER Benchmark, ERASER Project. eraserbenchmark.com [10] e-SNLI Dataset
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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engineering (elkraft). Experience in cybersecurity incident management. Experience in machine learning/artificial intelligence methods. Experience in simulation and modeling. Applicants will be assessed
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datasets, performing statistical analyses and machine learning approaches to identify biomarkers of treatment responses. The candidate will also develop and implement bioinformatics pipelines in a high
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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. We are currently seeking a Research Fellow with experience in AI and machine learning research and development, with a focus on any or all of following application areas: Computer vision Generative AI
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Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development