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Field
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and performance enhancement of power electronic converters and transformer-based solutions. Perform hardware-in-the-loop simulations, prototype development, and bench-level testing to validate design
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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-the-loop simulations, prototype development, and bench-level testing to validate design concepts and control implementations. Review and analyze relevant literature, industry standards, and emerging
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. The Digital and Computational Pathology Laboratory in PCS is seeking a talented and motivated postdoctoral fellow to develop cutting-edge methodologies in computer vision and AI to power whole slide image
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targeting neuroreceptors and neuroimmune pathways. The successful candidate will employ a combination of in vitro tissue assays and in vivo imaging techniques to characterize the pharmacological properties
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(fluorescence and Raman), and photon-sparse imaging research. Unit URL https://www.uidaho.edu/sci/physics Position Qualifications Required Experience A solid background in optics, imaging, biological physics, and
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new cell-based experiments to effectively and efficiently address the needs of the postdoc project Support evaluation of cell-based technologies including image analysis, transcriptomics, and biomarker