Assistant Professor in Machine Learning and Industrial Optimization

at  The University of Calgary

DESCRIPTION OF POSITION

The successful candidate should have a PhD in mathematics, operations research, or a related discipline with, and display potential for leading a high-impact, transdisciplinary research program at the interface of optimization and machine learning/data science, with an eye to industrial applications and the solution of read-world problems. A record of research excellence commensurate with their stage of career development and potential for attracting and sustaining external funding are also required.
Research Expectations: The successful candidate will be expected to secure external research funding from a variety of sources, to disseminate research results through various channels, e.g., publication in scholarly journals, submission to regulatory and policy documents, and participation in public forums. We place value on diverse activities like contribution to resource development (databases, protocols, software, etc.), entrepreneurship, social action, and fostering relations with relevant partners (e.g., government, indigenous groups, industry, academic, etc.).
The candidate must have strong interests in supporting the Faculty of Science¿s Data Science research initiatives and training programs. The candidate is expected to contribute to the University of Calgary¿s Ahead of Tomorrow strategic plan to promote transdisciplinary scholarship and innovation and empower our students and communities to tackle society¿s big problems.
Teaching Expectations: The successful candidate will play an important role both in teaching and in research supervision of undergraduate and graduate students. Evidence of (potential for) excellence and growth in these areas is an asset. The candidate will be prepared to teach courses in programs offered by the Department of Mathematics and Statistics, with a focus on undergraduate data science.
Service Expectation: Finally, the candidate should provide evidence of collegiality within their organization as well as commitment to equity, diversity and inclusion (EDI). They will be expected to fully engage in departmental and university service at the University of Calgary and work effectively with people from diverse communities and cultures.

DESCRIPTION OF WORK ENVIRONMENT

Successful applicants will become a faculty member in the Department of Mathematics and Statistics. They will be integrated into the broad spectrum of research activities in the Faculty of Science and across the university touching on data science, machine learning, optimization, and operations research.
The Faculty of Science and Departments in the Faculty of Science are committed to supporting interdisciplinary research and teaching and we offer and support opportunities to improve your research and your teaching practice by working with people in other disciplines. The Taylor Institute for Teaching and Learning also offers professional development and additional resources for those who are developing new ways to teach.
The University of Calgary adheres to the principles of the San Francisco Declaration on Research Assessment (DORA) and strives to evaluate research impact responsibly and inclusively by explicitly considering discipline-appropriate methods for demonstrating the quality, significance, and societal impact of a broad range of research contributions.
The Faculty of Science and Departments in the Faculty of Science are continuously improving our environment and culture to foster an inclusive environment where all department members can participate and collaborate to their full potential.
The University of Calgary¿s comprehensive benefits and pension program is designed to promote a productive level of health and well-being to staff members through coverage for health, dental, life insurance, income protection for disability, and retirement income planning. To learn about our comprehensive benefits package, please visit http://www.ucalgary.ca/hr/academicbenefitspension

Please refer the Job description for details