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Field
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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working with industry partners to translate this new technology into commercial sensors for vineyard installation. Further opportunities involve integrating the research data into the National Smoke
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, the appointed candidate will work closely with the line manager to develop novel control algorithms in EAP soft robotics combining Gaussian Predictors, hands-on laboratory experiments and JULIA computing
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at Centre for Space Sensors and Systems (CENSSS) at the Department of Technology Systems (ITS). Starting date no later than November 1, 2025. The fellowship period is three years. A fourth year maybe
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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or use existing simulation platforms to validate the developed algorithms and models. Analyse simulation data, and create visualizations to support research findings. Design and build prototypes
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implementation; b) monitor the project implementation details; c) develop control algorithms and digital twin models; d) guide prototype development and laboratory testing; e) analyse test data and
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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
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patch. Allergic contact dermatitis (ACD) to components of glucose-monitoring sensors and insulin pumps is increasingly problematic as disease management becomes dependent on wearable technologies. The
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are not limited to: network architecture design for NTN and terrestrial network (TN) convergence, intelligent traffic steering algorithms between TN and NTN, orchestration of TN/NTN resources for end-to-end