Assistant Professor Tenure-Track – Artificial Intelligence and Machine Learning

Assistant Professor Tenure-Track – Artificial Intelligence and Machine Learning

Requirements

• Earned doctorate in Computer Science, Artificial Intelligence, Machine Learning or closely related areas, from an accredited university.
• Excellent written and oral communication skills in English.

Responsibilities

• Plan, prepare, and deliver lectures, seminars, and/or laboratory offerings to undergraduate and/or graduate students.
• Assess student performance, provide timely feedback, and evaluate student learning outcomes to inform teaching practices and curriculum improvements.
• Engage students in active learning, foster critical thinking skills, and provide opportunities for discussion, collaboration, and hands-on learning.
• Adhere to university policies, academic integrity standards, and accreditation requirements related to teaching responsibilities.
• Incorporate innovative teaching methods, technologies, and pedagogical approaches to enhance student learning and engagement.
• Engage in ongoing professional development activities to stay current in the field, improve teaching skills, and explore new instructional strategies.
• Collaborate with colleagues, departmental faculty, and university staff to coordinate course offerings, share resources, and contribute to program development and improvement.
• Participate in institutional service roles including but not limited to, college committees, advisory committees, student/faculty recruitment, and mentoring of new and part-time faculty.
• Maintain an active agenda for research and scholarship, appropriate to the academic discipline.
• Make original contributions to the advancement of knowledge in the discipline through scholarly publications, presentations, or exhibitions.
• Demonstrate potential for external funding, national impact, or professional recognition.

Preferred Qualifications

• Demonstrated expertise in one or more of the following areas: Traditional AI and/or ML algorithms, Hardware-implemented AI/ML, Edge AI, AI for Internet-of-Things applications, Reinforcement Learning algorithms, Robotics.
• Established research in core AI theory and the development of novel algorithms or techniques.
• Successful track record of contributions to the fields of AI and/or ML, demonstrated by publications in relevant conferences and journals.
• Demonstrated university-level teaching and mentorship experience, particularly with graduate level students.