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novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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by Dr. Jeff Biernaskie (scRNAseq/ATACseq, U of C) and Dr. Grant Gordon (in vivo 2-photon imaging, U of C), Dr. Tomas Nowakowski (viral genomic technology to study the human brain, UCSF). Project
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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic
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from motion blur, defocus, and imaging artefacts, which hinder accurate diagnosis. This project aims to restore image clarity by designing intelligent algorithms that recover fine anatomical details
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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skills and be interested in developing a collaborative program of applied research in robotics. For example, this may include sensor development, applied robotic perception, algorithm development, or other