24 digital-image-processing-phd-scholarship Fellowship positions at Monash University
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Research Fellow - Advanced Signal Processing and Machine Learning Techniques for Vital Signs Measurement from Video Images Job No.: 691722 Location: Clayton campus Employment Type: Full-time
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to accelerate digital innovation and research capabilities within the Austin Hospital ICU. In this 0.5 FTE, Level B research-only role, you will play a key part in transforming clinical data into actionable
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“Revealing Order in Organic Semiconductors with Cryo-Electron Microscopy.” The successful candidate will apply and develop advanced cryo-electron diffraction and imaging methods to uncover structure–property
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models Applying imaging modalities to support drug distribution and efficacy studies Collaborating with national partners across a multidisciplinary research network This position offers a unique
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multi-organ-on-a-chip platform. Leveraging advanced microfluidics, live-cell imaging, and integrated biosensing technologies, you will help generate multi-parametric insights into barrier integrity
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Demonstrated awareness of confidentiality and responsible data handling Experience in cell signalling, cellular imaging or mouse models of cancer will be highly regarded. About the Institute and Department
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will have: A PhD (or equivalent experience) in medicinal or synthetic chemistry Strong hands‑on laboratory experience in synthetic organic or medicinal chemistry Excellent analytical, organisational, and
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brain state, maintain barrier integrity, and contribute to neurological disease, including brain cancer and stroke. The lab integrates advanced imaging, omics approaches, mouse genetics and flow cytometry
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activities. The successful candidate will have: A PhD qualification in medicinal or organic chemistry Experience in the synthesis of small molecules An understanding of pharmacology and/or drug development
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Science and Artificial Intelligence, with a focus on visual reasoning and robotic systems. The Research Fellow position involves developing novel approaches that integrate computer vision, natural