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machine learning. You enjoy applying these modelling skills to analyse and explain interactions within environmental and ecological systems Able to handle and integrate multi-temporal and multi-spatial data
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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beginning in fall 2026, preferably with an interest or focus on statistical foundations of data science, artificial intelligence and machine learning, theoretical statistics and probability, uncertainty
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 7 hours ago
artificial intelligence (AI) and statistical programming skills in R and/or SAS by implementing end-to-end machine learning pipelines, including data preprocessing, model training, cross-validation, simulation
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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, epidemiological, computational, machine-learning, and AI-based approaches to address biological and clinical research questions. Translate complex research questions into robust and reproducible analytical
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of measures, optimal transport, partial differential equations, and variational approximation methods, with potential applications to optimization and machine learning. The successful candidate will work in an
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cutting-edge research is carried out on a wide range of topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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for the AiTASHA project. We welcome applications from candidates who: - Have research expertise in statistics with a strong mathematical foundation. - Have experience in AI, machine learning or deep learning, and