Employment in the Fariborz Maseeh Department of Mathematics + Statistics

Portland State University is Oregon’s most affordable public research university, located in the heart of one of America’s most dynamic cities. Our mission to “let knowledge serve the city” reflects our dedication to turning ideas into action — in Portland and around the world. The city is our campus, giving student’s unmatched access to career connections, a vibrant cultural scene and hands-on learning experiences with hundreds of community partners.

More than 27,000 students from all backgrounds bring diverse perspectives to our classrooms and campus life, from the tree-lined Park Blocks to the bustling Urban Plaza and state-of-the-art science labs. We are proud of our world-class faculty, groundbreaking research and international reputation for excellence in sustainability, community engagement and innovation.

The Fariborz Maseeh Department of Mathematics and Statistics is part of the College of Liberal Arts and Sciences and is a premier provider of graduate education and training in Mathematics, Statistics, and Mathematics Education in the Portland metropolitan area. 

New Employee Onboarding

Faculty Positions

Teaching Assistant Professor in Data Science

The Fariborz Maseeh Department of Mathematics and Statistics at Portland State University invites applications for a non-tenure-track Teaching Assistant Professor in Data Science to begin in Fall 2024. For information about continuous appointments, please refer to Article 18 Section 2 of the AAUP collective bargaining agreement : https://www.pdx.edu/academic-affairs/sites/academicaffairs.web.wdt.pdx.edu/files/2023-02/AAUP%20CBA%202021-2024%20Watermark%20Art%2018%20Updated%20Amendment%202022

This is a full-time, teaching-focused position aimed at candidates with an excellent foundation in data science, with expertise in statistics and an ability to enhance our BS Data Science program. The ideal candidate will have proficiency in applying analytical tools in Python and/or R in an educational context, and be passionate about developing and/or delivering a comprehensive data science education at the undergraduate level.

A Ph.D. in Mathematical Sciences, Statistics, Data Science or equivalent is required. Demonstrated experience in teaching data science related courses at the undergraduate level, with a capacity to strengthen statistical content is expected.

Reflecting our departmental and institutional values, we strongly encourage applicants from underrepresented minorities to apply. Candidates must demonstrate their ability to advance the department’s commitment to diversity and inclusion.

The primary responsibilities include:
● teaching and developing a variety of undergraduate data science courses, with a particular focus on integrating statistical concepts and methodologies;
● collaborating in curriculum development to ensure a robust and dynamic data science program;
● engaging students with hands-on and applied learning experiences, utilizing Python and/or R;
● mentoring students in their academic and project work, emphasizing the practical application of statistical methods in data science;
● fostering an inclusive and supportive educational environment.

This faculty member will typically be responsible for teaching 3 courses each of the 3 quarters with some participation in department/university service. This is a continuous appointment with the possibility to apply for promotion to Teaching Associate Professor & Teaching Professor.

Minimum qualifications: PhD in Mathematical Sciences, Statistics, Data Science or equivalent

Apply at https://jobs.hrc.pdx.edu/postings/43929. Inquiries may be directed to the department manager at mathstat-dept-mgr@pdx.edu.
 

Academic Year 2023-2024 Adjunct Faculty Instructor Pool

Position Description:

The Maseeh Department of Mathematics and Statistics invites applications for New Adjunct Instructors with expertise in Mathematics or Statistics to teach undergraduate courses. Successful applicants will be expected to teach primarily lower division courses, including one or more of the following (see the PSU Bulletin for individual course descriptions):

100-200 Level Math Courses: 
Intermediate Algebra, Excursions in Mathematics, Introductory College Mathematics, Foundations Of Elementary Mathematics, Calculus, Applied Differential Equations, Introduction to Linear Algebra, Mathematical Computing

100-200 Level Stat Courses:
Elementary Data Analysis, Application of Statistics for Business, Introduction to Probability and Statistics

Depending on expertise, applicants may have an opportunity to teach some of our upper division mathematics and statistics courses.

Minimum Qualifications: M.S. or M.A. in Mathematics or Statistics (or closely related field). Previous teaching experience.

Adjunct Instructors are paid per credit hour, per the PSUFA Collective Bargaining Agreement, and are hired on either a per quarter or an academic year basis.

Application:

For New Adjunct Instructors:

Complete the on-line application at https://jobs.hrc.pdx.edu/postings/40858 and include the following:

• a curriculum vita,
• a letter of application, which should include: (1) a statement of teaching interest, (2) a suggestion of desired courses, (3) evidence of any teaching experience and effectiveness  (for example, any of the following: summary of evaluations, a letter or statement from a supervisor/person familiar with your teaching, a positive assessment, an award, etc), and (4) any significant constraints on availability,
• names and contact information for two professional references with knowledge of the candidate’s teaching qualifications.

For Returning Adjunct Instructors (who had a teaching assignment within the past two years): Private initiation link only, please contact Kelli Martin, Department Administrator at mathstat-dept-mgr@pdx.edu. 

RTG Postdoctoral Scholar

We are seeking postdoctoral candidates interested in working in our team RTG environment. Candidates whose doctoral research experience intersects with at least one of Areas I, II, III, and who are desirous of acquiring skills in the remaining areas, are sought. NSF requires all RTG postdocs to be US citizens, nationals, or permanent residents.

In the CADES program, trainees develop skills not solely for academia, but also for research careers in industry and government labs. This RTG specifically aims to produce researchers (a) trained in both mathematical and statistical techniques requiring advanced computational tools, (b) exposed to both disciplinary advances and real-world data, and (c) able to communicate both mathematically and in application-specific language. To this end, innovative training structures, seldom found in mathematics departments, are now being built at the Fariborz Maseeh Department of Mathematics & Statistics for RTG postdocs and students.

The department has previously leveraged large philanthropic investments to successfully recruit leading scholars in computational science. It offers growth opportunities in a nurturing atmosphere where senior faculty are invested in collaborative group building and mentoring. The department is housed in a newly renovated LEED-certified modern facility in downtown Portland, well-connected by rail, streetcar, and bus lines. The Portland area, home to numerous software and hardware companies in the high-tech sector, offers a thriving, progressive, urban scene, in close proximity to stunning nature.

The proposed start date is is March 18th, 2024. The Hiring Manager is flexible on the start date and other options are June 17th, 2024, or September 16th, 2024.

Minimum qualifications:

Ph.D. in mathematics, statistics or a closely aligned field in computational science, obtained prior to the start date of the appointment.

Apply at https://jobs.hrc.pdx.edu/postings/43157. Inquiries may be directed to Professor Jay Gopalakrishnan, grant PI, gjay@pdx.edu. 

Research Associate

The Research Associate position involves design, analysis and implementation of algorithms for solving problems in areas of large-scale scientific computing combined with state-of-the-art deep learning data science methodology. The specific fields include but are not limited to:

(i) Scalable Solvers for (Very) High-Order Finite Element Problems
(ii) Multilevel Methods for Training Encoder-Decoder Recurrent Neural Networks for Large-Scale Data Sets.

The work is a collaboration with teams of researchers at Lawrence Livermore National Laboratory.

It is expected that the suitable candidate will pursue independent but complementary research, contribute to project progress reports, give presentations at conferences and publish in peer reviewed specialized numerical analysis/scientific computing/data science journals.

Minimum qualifications:

• PhD or another appropriate combination of educational achievement and professional expertise.
• Knowledge of finite elements (f.e.) theory, some knowledge of deep learning algorithms, experience with publicly available scalable f.e. libraries, as well as proficiency in parallel computing and expertise in the programming languages C++ and PyTorch are required.

Apply at https://jobs.hrc.pdx.edu/postings/41510. Inquiries may be directed to Professor Panayot Vassilevski, grant PI, panayot@pdx.edu. 

Staff Positions

No staff positions are available at this time.