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ASSISTANT TEACHING PROFESSOR-Lecturer with Potential Security of Employment in Machine Learning

Recruitment Period

Open date: November 8th, 2019
Next review date: Sunday, Jan 5, 2020 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Tuesday, Jun 30, 2020 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Description

TENURE TRACK ASSISTANT TEACHING PROFESSOR OPENING IN MACHINE LEARNING, DEPARTMENT OF COGNITIVE SCIENCE, UC SAN DIEGO

The Department of Cognitive Science http://cogsci.ucsd.edu within the Division of Social Sciences at the University of California, San Diego is committed to academic excellence and diversity within the faculty, staff, and student body. We invite applications for a Tenure Track Assistant Teaching Professor position in Machine Learning. We interpret this area broadly and invite candidates who can provide students with strong foundations in machine learning, deep learning, neural networks, and/or visual computation. We are especially interested in candidates who will flourish in a Cognitive Science Department, and whose research, teaching, or service has prepared them to contribute to our commitment to diversity, inclusion, and equity within an academic setting. Joint appointment with other departments can be considered where appropriate.

The Assistant Teaching Professor is also known within the UC as an LPSOE (Lecturer with Potential for Security of Employment). The LPSOE series parallels the research-focused series but with emphasis upon excellence in teaching and other instruction-related activities. Individuals in the position are expected to provide outstanding teaching as well as to engage in professional activity (which can include research on pedagogy) and service related to the pedagogical mission of the department and university. This appointment confers membership in the Academic Senate.

Master’s Degree, Doctorate, or equivalent degree in Machine Learning, Cognitive Science, Computer Science, Engineering, Data Science or related field required by the start of appointment

An interdisciplinary perspective and experience with multiple methodologies is highly valued. The preferred candidate will have strong, demonstrated accomplishments in areas contributing to diversity, equity, and inclusion, and a desire to play a leadership role in advancing UC San Diego’s commitment to achieving excellence and diversity. Candidates will be favored who will contribute to instruction-related activities (e.g., conducting TA training, curriculum development, creation of instructional materials including online instruction, employing new technologies) at the campus, statewide, and national level.

All applications must be submitted through https://apol-recruit.ucsd.edu/JPF02330.

UCSD is an Equal Opportunity/Affirmative Action Employer with a strong institutional commitment to academic excellence and diversity.

Applicants with interest in dual career resources may visit the Partner Opportunities Program website.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, or protected veteran status.

Job location

San Diego, CA

Learn More

More information about this recruitment: http://www.cogsci.ucsd.edu/

Requirements

Document requirements
  • Cover Letter - Describe background and interests

  • Curriculum Vitae - Your most recently updated C.V.

  • Statement of Scholarly Activities - May include research on pedagogical focus.

  • Documentation of Teaching Excellence - e.g. links to instructional materials developed by candidate, summaries of teaching evaluations, on-line instructional segments

  • Publications - Include up to 4 representative publications/scholarly articles particularly with a pedagogical focus.

  • Statement of Contributions to Diversity - Applicants should summarize their past or potential contributions to diversity. See our Faculty Equity site for more information.

  • Misc / Additional (Optional)

  • Misc / Additional (Optional)

  • Misc / Additional (Optional)

Reference requirements
  • 3-5 letters of reference required