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, traditional planning often fails to capture workload variability, uncertainty, and the complex interaction between product features, labor availability, and machine capacity. Your PhD will address
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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pangenomics), quantum-based simulation methods for drug design, or quantum machine learning for large omics datasets. The candidate is expected to have acquired first teaching experience, and first experience
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triggered by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate
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that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding
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physiological signals, with a focus on ECG, and to develop machine learning and deep learning methods for classifying clinical, health, and wellness findings. Supporting project management and research group
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore
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the PhD program (https://www.utrgv.edu/cla/academic-programs/clinical-pyschology-phd-program/index.htm) mentor graduate students in the PhD program in clinical psychology, and serve as a clinical supervisor
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projects. Develops research plans and budgets, designs and implements statistical, machine-learning, and bioinformatic methodologies, and manages project workflows while ensuring all sponsor requirements and