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Field
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/Anders Lien 10th April 2026 Languages English English English PhD Research Fellowship in Quantitative Imaging and Cell Mechanics of Early Embryo Development Apply for this job See advertisement About the
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integrates behavioural and cognitive assessments with neuroimaging and AI, including neuropsychological testing, cognitive tasks, functional Magnetic Resonance Imaging (fMRI), structural MRI, and predictive
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TEC. 2. OBJECTIVES: The NeuroEngineering Laboratory at C-BER has extensive experience in biomedical image analysis from different clinical contexts, ranging from the analysis of motor seizures in
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and make particle beams more accessible for uses such as medical imaging. However, reaching very high particle energies in plasma accelerators is challenging. We are offering a postdoctoral position to
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specific neuronal types and develop new technologies allowing us to selectively target the unique receptor populations. To achieve this, we are using imaging and molecular techniques to measure receptor
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, remote sensing and photointerpretation, field data collection with mobile devices, AI-based image and text analysis). 2. Collection, organization, and spatialization of data on human perceptions and
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closed-loop interventions for health, with extensions to robotic control. The core technical deliverable is a working prototype chip validated for bilateral gait and balance monitoring under controlled lab
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 9 days ago
supervision, publication and publicity activities, code development, and collaboration with industry, open source, and academic partners. The project goal is to develop and demonstrate a stochastic design
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based on the peer-to-peer concept; (2) designing and developing the hardware required to build a functional prototype of a communication module, ensuring its reliability, scalability, and compatibility
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and brain activity. Ongoing projects include: Large-scale naturalistic image and video datasets (including curated “visual experience” style datasets; indoor/outdoor scenes, places, objects, and actions