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Field
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machine learning methods, particularly large language models (LLMs), to marketing research. Applicants are invited to contact Prof. Edward Lai at telephone number 2766 7141 or via email at edward-yh.lai
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sequencing, proteomics, and metabolomics; interpretation of datasets and clinical data using advanced statistical methods and machine learning algorithms to identify correlations between molecular alterations
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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research-operational partnerships and learning about systems involving forest fuels and fire emissions modeling. They will gain experience with modeling, coding, and database management in support of a
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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a
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of Informatics, Uni-versity of Oslo, and will be part of a growing research agenda at the intersection of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as