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, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
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vision and/or biomedical image analysis is essential, as well as the ability to manage own academic research and associated activities. Informal enquiries may be addressed to Jens Rittscher (jens.rittscher
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for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data integration
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for self-driving and/or human-in-the-loop experiments; (3) computer vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading
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coastal hotspot areas, will embark on this innovation journey. Working within stakeholder alliances based on trust, they will co-design regenerative resilience visions, chart adaptation pathways, and
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Researcher will lead and support experimental research on visual and perception and decision-making in complex environments, with a focus on medical image perception and Artificial Intelligence (AI
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data and report findings in papers. Visa sponsorship is not available for this position. Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and
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identifying the ideal HWO spectral range(s) and resolving power(s) for understanding terrestrial and sub-Neptune planetary environments using high contrast imaging techniques. Candidates working anywhere in
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cells perform over time in controlled in vitro settings, biomaterial-supported culture environments, and prototype device-relevant configurations. The position requires a technically mature researcher who
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and aspects related to safety and best practices. The candidate will make extensive use of state-of-the-art imaging, spectroscopy (Raman, electron microscopy, infrared, etc.) and scattering methods