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Field
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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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, including survival analysis, time-series techniques, causal inference approaches, and/or machine learning methods to large healthcare datasets. Prior experience mentoring or supervising graduate students
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% position is available at the University of Agder, Faculty of Engineering and Science as a PhD Research Fellow in ICT, affiliated to the Department of Information and Communication Technology at, for a
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23 Mar 2026 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions Application
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, Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning , Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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Fusion Tribrid MS and Waters Q-ToF instruments are highly desired. Experience handling and analyzing large-scale MS, MS(MS) and/or proteomics-like datasets using statistical and machine learning techniques
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper