About this position

Position Title Visiting Assistant Professor of Artificial Intelligence and Machine Learning Job Description The Department of Natural Sciences and Environmental Health at Mississippi Valley State University invites applications for a full-time Visiting Assistant Professor of Artificial Intelligence and Machine Learning (AI/ML). The successful candidate will teach graduate-level courses in Artificial Intelligence and Machine Learning, with a particular emphasis on applications in Biomedical Sciences, Environmental Science, and Environmental Public Health, as well as undergraduate courses as needed. We seek an enthusiastic, innovative, and student-centered educator who is passionate about advancing AI education, mentoring students, and applying emerging technologies to real-world scientific and public health challenges. The selected candidate will have the opportunity to contribute to the University’s new interdisciplinary Master’s program in Artificial Intelligence and Machine Learning for Biomedical and Environmental Health Systems ( AIM - BEHS ), helping prepare the next generation of professionals to use AI-driven approaches to address critical challenges in healthcare, biomedical research, environmental sustainability, and public health. Required Qualifications Applicants must possess the following qualifications: • A Ph.D. in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Computational Science, Biomedical Sciences, Environmental Science, Environmental Health, Environmental Public Health, Engineering, or a closely related field by the start date of appointment. • Demonstrated expertise or a strong academic or research background in AI, Machine Learning, Data Science, or computational methods, with the ability to apply these technologies to biomedical, environmental, or public health challenges. • Demonstrated ability or strong potential to teach graduate-level courses in Artificial Intelligence, Machine Learning, and related disciplines, including interdisciplinary applications. • Evidence of scholarly achievement, research experience, or strong potential to develop research projects and engage graduate and undergraduate students in research and applied learning. • Ability and willingness to collaborate with faculty and researchers from diverse scientific and technical disciplines. • Strong communication, organizational, and interpersonal skills, with a commitment to effective teaching, student mentorship, and academic excellence. • Commitment to supporting student success, interdisciplinary education, scholarly activities, and university service. Preferred Qualifications The following qualifications are desirable but not required: • Teaching, research, postdoctoral, or relevant industry experience in Artificial Intelligence, Machine Learning, or Data Science. • Experience with AI/ML tools and programming environments such as Python, R, TensorFlow, PyTorch, or related technologies. • Experience applying AI/ML techniques to biomedical research, Bioinformatics, healthcare, environmental monitoring, environmental health, or public health data. • Experience developing interdisciplinary research projects, mentoring students, or collaborating on externally funded research. • Familiarity with emerging AI technologies, including deep learning, generative AI, and advanced data analytics. • Interest in contributing to the development and growth of a new interdisciplinary graduate program. Special Instructions to Applicants Application Materials Applicants should submit the following: • Curriculum vitae. • One combined document that includes: • Cover letter describing teaching and research interests. • Statement of teaching philosophy. • Statement of research interests, including plans for student involvement. • All graduate transcripts (unofficial transcripts are acceptable for initial review). • Names and contact information for three professional references. • Three professional reference letters. Equal Opportunity Statement The university is an Equal Opportunity Employer and encourages applications from candidates who are committed to teaching, mentoring, research, and supporting students from diverse backgrounds.

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