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Website Link https://phoenixmed.arizona.edu/cts/phd Location Greater Phoenix Area Address Phoenix, AZ USA Position Highlights A full-time Postdoctoral Research Associate position is available in
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it relates to the laser process parameters. Specifically, you will carry out high resolution Raman imaging on laser written polymer networks with liquid crystal resins. Additionally, you will develop
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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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, and positioning for subsequent fundraising and go-to-market activities Actively contribute to securing follow-up funding together with the fundraising team Your profile University degree (MSc or PhD) in
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stressed by the fickleness of the unknown, you will put your organisational skills to good use to drive the practical aspects of the project. You also have: A PhD in (Bio)Chemistry, Biocatalysis, or a
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 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
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Applicable Driver's License: A driver's license is not required for this position. More About This Job Required Qualifications: The applicant should have a PhD in biological/biomedical sciences, or an MD with
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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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working with HPV-positive cell lines is a plus. Preferred Schedule: 7am to 5pm Position Requirements: Minimum Qualifications: Minimum education: PhD in a scientific field or MD Minimum experience: 0-3 years
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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