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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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- focusing on foundational logic and systems, strategic implementation and emerging technology. This is a full-time, non-tenure track position. Key Responsibilities: Teaching Teach four courses per semester
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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initiatives and significant growth in the schools industry based development support. Responsibilities Required Education: Bachelor's Degree in a related area Preferred Education: Doctoral Degree MBA or PhD in
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, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7
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research profile to further integrating wet-lab techniques (such as single-cell sequencing, -omics) with advanced data analysis, for example through bioinformatics, machine learning, or AI. Themes such as
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about the lab at: https://mbzuai.ac.ae/study/faculty/natasa-przulj/ and https://przulj-lab.github.io/ Qualifications PhD in Computer Science, Mathematics, Physics, Bioinformatics, or a related
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Understanding how low UPF1 expression leads to drug resistance in bladder cancers School of Medicine and Population Health PhD Research Project Self Funded Dr Ruth Thompson, Prof Syed Hussain
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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Babinski, PhD. This position will report to Research Professor Leslie Babinski. CIDR is a school-based research and professional learning initiative designed to strengthen adolescent wellbeing and literacy