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to September 2031 Closing date: 20th October 2026 There are 2 positions available Background Vasculogenic mimicry (VM) is the process by which tumour cells imitate blood vessels allowing tumours to grow far
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analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities Plan and execute experimental workflows to achieve project
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Institutions. Preference factors: Previous experience in biomedical image and signal processing; Previous experience in computer vision; Minimum requirements: Degree in Computer Science, Informatics Engineering
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-UAS technologies o Multi-agent systems and swarm robotics o Autonomous navigation and guidance o Computer vision and perception o Machine learning
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 11 hours ago
. recipients with an opportunity to develop further the research skills acquired in their doctoral programs or to learn new research techniques, in preparation for an academic or research career. In the process
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technologies (e.g., TIRF, PALM, FCS) to ongoing studies of human retrovirus (HIV & HTLV) particle biogenesis. Successful recruits will acquire extensive training in quantitative fluorescence imaging, and will
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at the intersection of marine ecology, ocean technology, machine learning, and high-throughput biological imaging. This position offers a rare opportunity to help pioneer the use of advanced shadowgraph imaging systems
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In this role you will support the delivery of two exciting, EPSRC-funded research programmes at the University of Nottingham, working across AI, human-computer interaction, and creative technology
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will work with Rita Pucci (computer scientist) and Luna van der Loos (seaweed biologist and taxonomist). You will also be part of our PhD and postdoc community — a close-knit group of around 50 PhD
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. · Knowledge of molecular biology, cell biology, immunology, neuroscience, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning