27 machine-learning-phd positions at University of Texas Rio Grande Valley in United States
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an MD or PhD in Biochemistry, Neuroscience, Microbiology, Immunology, Genetics/Genomics, Biomedical Sciences, Biology, or a related field of study from an accredited institution or a terminal degree in
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the area of Clinical Psychology. beginning in the 2026-2027 academic year. We are interested in experts in any area of clinical psychology, including neuropsychology. The department offers a PhD program in
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or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience in interdisciplinary collaboration
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journals or conferences; experience with or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience
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, with potential teaching opportunities in the English M.A. (pending qualification to teach at the graduate level), along with service to the department, university, field, and/or community. There is no
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funding. Teach undergraduate and/or graduate courses effectively. Participate in departmental and university service. Scholarship: Initial publications in peer-reviewed journals. Engagement in professional
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at the undergraduate and/or graduate level. Preferred Qualifications: PhD or DBA in Management or related field Three years of college teaching experience Ability to teach online or use technology to support teaching
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research spanning theoretical foundations, bioinformatics, machine learning, robotics, data mining, and applications of Computer Science. Together, the programs prepare students for graduate study in
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alloys for energy applications in harsh environments using additive manufacturing. This research involves integrating computational modeling, machine learning, and experimental investigations to design and
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the participation of all health-related programs and create a stronger framework for collaboration and impact among health-related programs. The goal is for the division to boost interdisciplinary learning, enhance