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Field
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algorithms to develop cybersecurity, optimization, and control solutions for real-world grid applications. Candidates will be required to work in at least 4 of the following areas: Build, simulate, and
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design and performance analysis, Proven track record
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems
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algorithms for advanced and multi-modality imaging. Work on system designs, physics models, computation, benchtop experiments with small animals, and clinical translation. Additional areas of interest are in
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activity in ulcerative colitis patients with transcriptional changes in a longitudinal patient cohort, develop deconvolution algorithms, extract features from H&E sections etc. Bacterial metabolism and host
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial
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in the design, implementation, and maintenance of data pipelines and leading/assisting in building algorithms for deep learning with close collaboration from the study team. While programming
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the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms