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processing, machine learning, computational imaging, and clinical translation. Beyond your individual research contributions, you will serve as a technical coach for the PhD researchers, helping to align
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learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies that amplify human potential. The successful candidate will engage
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network inference and modeling, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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will possess a PhD or equivalent doctoral degree in an engineering or physical science discipline with some research experience related to critical minerals. They must be able to communicate
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(there is room for learning-on-the-job). A PhD in aerospace/mechanical engineering or applied physics. Demonstrated ability to conduct research in experimental fluid mechanics. Proven competence on flow
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demonstrated through at least one first-author publication: machine learning/computer science, computational biology/bioinformatics, cheminformatics. Individuals should have or be expected to have a PhD with
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, Isomap), manifold learning, and machine-learning classifiers to extract neural geometry metrics from both species. Systematically compare behavioural and neural data across mice and humans, identifying
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clinical or imaging-based research. Familiarity with vascular imaging software, image post-processing, and AI/machine learning applications in clinical or imaging research is desirable. Demonstrates strong