251 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Pennsylvania State University
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and narrative) that communicates the lab's focus on Open-Source AI Systems Modeling & Computer Simulation. Develop engaging tutorials, workshop materials, and "project starter kits" to lower the barrier
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method widely used in social sciences, education, and business research. This project aims to advance AI applications in qualitative research while providing hands-on experience in machine learning
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/machine learning strategy development – with the ability to design, implement, and evaluate data-driven or AI-based strategies. Graduate student mentorship – providing technical and professional guidance
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) Program Courses within the program, particularly the cornerstone engineering design course, assists students in learning design processes, methods, and decision-making tools while engaged in team-based
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. Teaching topics may include, but are not limited to: Leadership/management Supervisory skills Computer literacy Project management Design thinking Foreign languages Artificial intelligence Addictions and
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/or competencies: Possess an understanding of youth development and be able to effectively teach and interact with youth. Possess knowledge of educational principles and presentation preparation skills
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research expenditures. We are seeking candidates with scholarship emphases in areas such as artificial intelligence (AI), data science, machine learning, and/or application areas of AI and DS. Candidates
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master’s degree in information systems, information technology, or a closely related field is required. Prior college-level teaching experience. Familiarity with AI concepts, including machine learning
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other duties of a similar nature. Applicants should have strong interpersonal, communication, and computer skills. Applicants with a background in any of the life sciences are preferred. This position
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. Additional opportunities will include working with students and postdocs in the lab to support the development of computer vision models for pest identification and to support the BeeSpatial web application