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Field
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architecture, testing/debugging skills, and version control. Experience applying machine learning, artificial intelligence, or statistical modelling techniques to digital forensics, online surveillance, or large
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of the Recovery and Resilience Program, through the Foundation for Science and Technology – FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering (Medical Informatics
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis
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enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build
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4 Sep 2025 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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. The position is based at the Department of Informatics, University of Oslo, and will be part of a growing research agenda at the intersection of geographic information systems, epidemiology, machine learning and
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
: PD&PEWER Level A&B Postdoctoral - Research Fellow _ CSS.pdf This position will provide opportunities to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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of these features for distinguishing attacks from normal behaviour, through statistical and/or machine learning-based analysis. • Analyse the applicability and potential adaptation of these features for anomaly