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Field
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and implement innovative image analysis methods to quantify plant characteristics. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and
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of optics, imaging systems, digital holography, speckle metrology, Matlab and simulations, signal processing, the Fourier transform, machine learning, and classifiers such as SVM. Meriting qualifications
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adapt to ever changing needs Preferred Qualifications: ● Research experience in natural language processing on biomedical and/or clinical texts, computer vision on medical images, causal inference
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3 Sep 2025 Job Information Organisation/Company University of Turku Research Field Computer science » Other Engineering » Computer engineering Mathematics » Computational mathematics Medical
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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vision, controls, cyber-physical systems and their security, hardware security, and machine learning and their security. The work will include algorithm design, prototype implementation (e.g., in Matlab
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Job Description Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in Computer Sciences, Engineering, Neuroscience, Psychology or a closely related
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or experience in nontraditional research publication methods and collaborative notetaking software (e.g., Roam Research, Obsidian, Notion). ? Familiarity with cloud computing and machine learning techniques
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in Computer Sciences, Engineering, Neuroscience, Psychology or a closely
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 6 days ago
://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic