College of IST Wage-Payroll Graduate Assistant

Job description

APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday. CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday. If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants . Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . JOB DESCRIPTION AND POSITION REQUIREMENTS The College of IST is seeking applicants for part-time job a part-time, wage payroll Graduate Assistant . The Wage-Payroll Graduate Assistant will contribute to an NSF-funded interdisciplinary research project investigating self-supervised visual learning inspired by human perception. Responsibilities may include developing and evaluating machine learning and computer vision algorithms; implementing and testing research software; preparing and processing image and video datasets; conducting experiments and statistical analyses; reviewing relevant scientific literature; assisting with studies as needed; documenting research findings; and supporting the preparation of manuscripts, presentations, and research reports. The student will work closely with faculty PIs (Profs. Brad Wyble of Psychology and James Wang of IST) and other students in a collaborative, interdisciplinary environment. Requirements, qualifications, and/or competencies: Current enrollment in a graduate program at Penn State (M.S. or Ph.D.). Background in artificial intelligence, computer vision, machine learning, or a closely related field. Strong programming skills in Python; experience with PyTorch, TensorFlow, or similar deep learning frameworks is preferred. Familiarity with computer vision, deep learning, or statistical learning methods. Experience with scientific programming, data analysis, or software development in a Linux environment. Strong analytical, problem-solving, and communication skills. Ability to work both independently and collaboratively within an interdisciplinary research team. Excellent organizational skills and attention to detail. Prior research experience in mechanical engineering, robotics, materials science, or related application domains is a plus, but not required. Prior research experience, publications, or open-source software contributions are desirable but not required. Compensation: The starting rate for this job is $18-20 depending on qualifications . BACKGROUND CHECKS/CLEARANCES Employment with the University will require successful completion of background check(s) in accordance with University policies. CAMPUS SECURITY CRIME STATISTICS Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here . EEO IS THE LAW Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473. Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university’s teaching, research, and service mission. Federal Contractors Labor Law Poster PA State Labor Law Poster Penn State Policies Copyright Information Hotlines