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Requisition Number: 2025 - 228 Business Unit: College of Medicine Department: Ob/Gyn Research Lab 50000603 Reg-Temp: Full-Time Regular Position Summary: Conduct a range of assays to assess the effects
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Position Description: Every great life-changing discovery begins the same way—with new knowledge. It can change everything, from a single life to the future of entire communities. That’s why
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understanding of the polymer network morphology and how it relates to the laser process parameters. Specifically, you will carry out high resolution Raman imaging on laser written polymer networks with liquid
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, Tucson, AZ 85721 USA Position Highlights The Department of Physiology in the College of Medicine - Tucson campus is seeking a Postdoctoral Research Associate I to join the lab of Associate Professor
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence
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on autofluorescence (AF) imaging and Raman spectroscopy for detection of metastatic lymph nodes during breast cancer surgery. Engaging with and reporting to Dr Alexey A. Koloydenko (Department of
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University of Chicago, Pritzker School of Molecular Engineering Position ID: UofC -PME -POSTDOC [#30355] Position Title: Position Type: Postdoctoral Position Location: Chicago, Illinois 60637
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position, please contact Dr Guillermo J Amador, assistant professor, telephone +31 317 482749 or by e-mail guillermo.amador@wur.nl. Questions about the procedure? Get in touch with Ine van 't Land, Corporate
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PhD (or thesis submitted subject to examination) in Earth or Materials Sciences with a focus on rock and mineral deformation processes Breadth of knowledge in physics of materials applied to Earth
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
develop our state-of-the-art imaging systems by optimizing the hardware and integrating real-time data processing and analysis through machine learning techniques to achieve precise characterization