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Job ID: 100120

Tenure Track Position in Software Engineering with Specialization in Big Data Analytics
Concordia University

  • Nov. 14, 2017
  • Tenure Track Position in Software Engineering with Specialization in Big Data Analytics
  • Department of Computer Science and Software Engineering
  • Concordia University
    Montreal, QC
  • Feb. 28, 2018
  • Jul. 1, 2018
  • Assistant Professor
  • Computer Engineering
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Tenure-track Position in Software Engineering
With Specialization in Big Data Analytics
Department of Computer Science and Software Engineering

One of Canada’s most innovative Faculties, the Faculty of Engineering and Computer Science (ENCS) is home to about 8,500 engineering and computer science students and a faculty complement of 208 faculty members. ENCS has 3,500 graduate students enrolled in more than 15 graduate programs. The Faculty’s research profile continues to grow as it fosters multidisciplinary approaches to finding solutions to a broad range of societal challenges. For more information on the Faculty, please visit:

The Department of Computer Science and Software Engineering has a faculty complement of 42 researchers active in the various areas of computer science and software engineering and its strength has rapidly been growing in recent years. The Department offers PhD, masters and bachelor’s degrees in both Computer Science and Software Engineering. Concordia University attracts high quality domestic and international students in all its programs and enrollments are stable and strong at both the undergraduate and graduate levels. More information on the Department is available at:

The Department of Computer Science and Software Engineering seeks an outstanding candidate for one tenure-track position in the area of Big Data Analytics. We will consider applicants in all research areas related to big data, but we are particularly interested in candidates who have conducted research in the theoretical or practical aspects of big data – algorithms, machine learning, deep learning, statistical learning methods applied to one or more domains – software engineering, bio-informatics, health care, imaging and video, social networks, natural language processing and others. The appointment is intended to be at the rank of Assistant Professor but exceptional candidates at the Associate level may also be considered.

Applicants should preferably have a PhD degree in software engineering or computer engineering or a related area and also possess research and development expertise in the area of big data and applications. A successful candidate is expected to provide academic leadership, establish a strong externally funded research program, demonstrated abilities to work in collaborative multidisciplinary settings and teach/develop both undergraduate and graduate courses. Strong commitment to the supervision of graduate student research and to excellence in teaching are essential. Relevant industrial experience is an asset. An undergraduate degree in engineering is preferable. Excellent communication skills are required. Membership or eligibility for membership in a Canadian professional engineering association, preferably in the province of Quebec, is required.

Applications should consist of a cover letter, detailed curriculum vitae, teaching and research statements, and the names of at least four referees. Applications will be reviewed on an ongoing basis until a suitable candidate has been identified, but should be submitted by February 28, 2018. Only short-listed applicants will be notified. The appointment is expected to commence in July 2018.
Kindly forward your electronic applications to:

Dr. Sudhir Mudur, Chair
Department of Computer Science and Software Engineering

  • All qualified candidates are encouraged to apply; however, Canadians and Permanent Residents will be given priority. Concordia is strongly committed to employment equity within its community, and to recruiting a diverse faculty and staff. The University encourages applications from all qualified candidates, including women, members of visible minorities, Aboriginal persons, members of sexual minorities, persons with disabilities, and others who may contribute to diversification.

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