Job ID: 131256

Assistant Professor - Computer Science

University of Kansas

  • Oct. 10, 2019
 
  • Assistant Professor - Computer Science
  • Electrical Engr & Comp Science
  • University of Kansas
    Lawrence, KS
 
  • Open until filled
  • Available immediately
  •  
 
  • Assistant Professor
  • Computer Science
 
 

Assistant Professor – Computer Science
Electrical Engr & Comp Science

Position Overview

The University of Kansas (KU) Department of Electrical Engineering and Computer Science (EECS) seeks outstanding individuals for a tenure track Assistant Professor position in Computer Science. Successful candidates are expected to contribute to the development of academic programs and contribute to the research community. Applicants are expected to have an earned doctorate, or equivalent in Computer Science, Computer Engineering, Electrical Engineering, or closely related fields at the time of appointment. The following position is open:

Computer Science: Applicants focusing on the transforming of data into knowledge via machine learning are encouraged to apply. Applicants areas of expertise/interest can range from classification to deep learning and neural networks, software/hardware implementation of machine learning algorithms, optimization and artificial intelligence, and/or data mining. Applicants pursuing research in areas that are synergistic with departmental strengths of security and assurance, imaging, scientific/parallel computing, communication systems and networking, radar and remote sensing, embedded systems, and signal processing are preferred.

Exceptional applicants in other closely related areas to the above topics may be considered.

All qualified (including international) candidates are encourage to apply. The successful candidate must be eligible to work in the U.S. by the effective date of the appointment.

The University of Kansas School of Engineering (SoE) and the EECS Department strongly value diversity, equity, and inclusion. In a continuing effort to enrich its academic environment and provide equal educational and employment opportunities, the university actively encourages applications from members of underrepresented groups in higher education. We seek candidates who can contribute to fostering an inclusive culture. KU SoE is one of only 29 universities to achieve the exemplar, inaugural ASEE Diversity Recognition Bronze Award https://diversityrecognition.asee.org/award-recipients/.

EECS is the largest department in the KU School of Engineering with 36 faculty members and a research volume of about $5.8 million per year. The EECS department offers undergraduate and graduate degrees in electrical engineering, computer engineering, computer science, interdisciplinary computing, and information technology. The department has approximately 800 undergraduate and 150 graduate students. EECS faculty engage in collaborative research projects across multiple areas of interest within the department and the University, and with other institutions. There are many interdisciplinary opportunities involving schools at KU and the KU Medical Center. The KU School of Engineering has added new faculty lines over a five-year period and has expanded research and teaching facilities.

Job Description

40% Teach undergraduate and graduate courses in the Electrical Engineering and Computer Science department. Participate in undergraduate and graduate student academic advising. Mentor graduate students in research and serve as the major advisor and chair of Master’s and Doctoral level thesis committees.

40% Perform and conduct research in the research centers/laboratories associated with the department. Establish a strong research agenda in areas of expertise that overlaps, supports, or expands the current research in the department, leading to external grant support and publication in recognized refereed journals and conference proceedings. Collaborate with faculty and professional staff on KU campuses.

20% Perform Department, School, University and professional service activities normally expected of university faculty commensurate with rank. Participating in local, national and international professional conferences and other activities appropriate to the discipline. Perform other duties as assigned by the Department Chair.

Required Qualifications

1. Earned doctorate, or equivalent in computer science, computer engineering, electrical engineering, or a closely related scientific or engineering discipline by the time of appointment.

2. Expertise in transforming of data into knowledge via machine learning and/or expertise in areas ranging from classification, deep learning and neural networks, software/hardware implementation of machine learning algorithms, optimization and artificial intelligence, or data mining. Expertise as demonstrated by scholarly work, publications, teaching and/or service.

Contact Information to Applicants

Erik Perrins
eecs_search@ku.edu

Additional Candidate Instruction

A complete online application will include the following items: 1) letter of application, 2) curriculum vitae, 3) a statement of research interests and plans, 4) a statement of teaching interests including plans for broadening participation in computing, and 5) contact information (including email addresses) for three individuals who can provide letters of recommendation. Candidates should address, in their application letter, how they plan to promote the values of diversity, equity, and inclusion.

In addition to the materials above, learning about each applicant’s contribution and engagement in areas of diversity is an important part of KU’s mission. As a result, applicants will be presented the following questions at the time of application. Each response must be within 4,000 characters or less.
Please describe your experience, approach or methodologies to create and implement inclusive teaching and advising practices, pedagogical techniques and/or course materials to create an inclusive classroom environment.
In your current and/or previous role, please provide an example outlining how you demonstrate your ongoing commitment in working with diverse groups/populations.
Review of application materials begins November 15, 2019 and continues as long as needed to identify a qualified pool of candidates.


 
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