Connectionists: [Endowed Associate/Full Professor] MATRIX AI Consortium (University of Texas at San Antonio) in Inclusive & Human-Centered AI

[Dhireesha Kudithipudi]

Over the past two years, The University of Texas at San Antonio (UTSA) has
made significant progress in interdisciplinary AI research and scholarship
efforts, notably through the newly launched MATRIX <https://ai.utsa.edu> AI
Consortium and establishing the School of Data Science.  UTSA has a focused
hiring plan in the area of Artificial Intelligence, specifically in
Inclusive & Human-Centered AI Systems and AI Accelerators, both of which
advance the research thrusts in the MATRIX <https://ai.utsa.edu/> . The
initiative will serve as the nexus for leading AI innovators to perform
transdisciplinary research, engage in high-impact partnerships, and provide
thought leadership and domain expertise to solve intractable problems in AI.

Highlighted position: Endowed Full Professor with a joint appointment in
the Department of Computer Science and the Department of Electrical and
Computer Engineering.
Qualifications

   1. *Inclusive and Human-Centered AI Systems: *Outstanding senior
   candidates are invited for an Endowed Full/Associate professor
position in “Inclusive
   and Human-centered AI Systems”. Human-centered AI, is a
   multidisciplinary effort to advance AI research and deployment in ways that
   dramatically amplify human performance in several settings. This area
   bridges the fields of AI models (*eg*: explainable AI, fairness in AI
   models, decentralized decision making, dynamic & adaptive models) with
   human engagement in high-consequence contexts. Researchers in this area
   will have the opportunity to collaborate with MATRIX partners in developing
   convergent AI solutions that are inclusive. Successful candidates will
   demonstrate (1) a record of high-quality research, publication record, and
   track-record of external funding in human-centered AI & related areas, (2)
   excellence in undergraduate and graduate education, and (3) a commitment to
   inclusion and diversity.

Responsibilities

Responsibilities include research (individual and collaborative), teaching
at the graduate and undergraduate levels, and program development.
Candidates for the endowed professor/associate professor should be creating
research products, expected but are not limited to communicating the
research project results in diverse academic outlets; contributing to
open-source scientific software, curated datasets, and/or blogs; and
developing thought leadership pieces with academic, government, and
industry partners.
Posting End Date

All applications received by *December 1st, 2021* will be given full
consideration. Applications received after that date will be accepted and
reviewed until the position is filled.
Required Application Materials

Applicants should submit their application packages via the UTSA HR
website: https://www.utsa.edu/hr/employment/

   - Curriculum Vitae
   - Research and teaching statements, which include discussion on the
role diversity
   and inclusion plays in an academic environment (3-page limit)
   - Complete contact information for at least three professional
   references

*Please submit all documents together in a single PDF in order to be
considered.*

Questions and nominations for any position should be sent to the Director
of MATRIX AI Consortium, Dhireesha Kudithipudi, Search Committee Chair at
dk at utsa.edu.
Required Qualifications

   - Doctorate degree in Computer Engineering, Computer Science, Electrical
   Engineering, and/or related fields.
   -  Appropriate research and teaching record for appointment at the rank
   for each position (for those seeking appointments with tenure, this is
   contingent upon Board of Regents’ approval)
   -  Demonstrated commitment to inclusion and diversity.
   - Moreover, the successful candidate(s) must demonstrate their ability
   to work with and be sensitive to the educational needs of diverse urban
   populations and support the University’s commitment to thrive as a Hispanic
   Serving Institution and a model for student success.
   - The most competitive candidates will also have experience
   in interdisciplinary research and/or curriculum development, and
   scientific open-source projects.

Preferred Qualifications

   - Ideal candidates include those who demonstrate evidence of a
   commitment to collaboration, diversity, equity, and inclusion through
   research, teaching, service endeavors, and those who can show a
   commitment to data-intensive research and software reproducibility.
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