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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, and safety profiles, and how these relationships can be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory
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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed
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candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with
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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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to treatment, population health monitoring, workforce development and leadership, policy, and advocacy. Background The Robotics, Autonomy and Machine Intelligence (RAMI) Group led by Prof Nabil Aouf is dedicated
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diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and
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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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projects across the following areas: Spatial and Single-Cell Proteomics in Childhood Cancer Cell-cell communication & cellular fitness in CAR-T & CAR-NK therapy Deep learning & LLMs in mass spectrometry data
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developing statistical and machine learning approaches for the integration of cancer multi-omics data and the analysis of CRISPR-based screens. Responsibilities include designing bioinformatics workflows
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess