Motorola Solutions Foundation Scholars Program
We are looking for bright undergraduate students to get involved in research in the areas of Artificial Intelligence, Bioinformatics, Medical Informatics, Robotics and Software Engineering that will start in January 2026. Selected students will receive a total stipend of $5,310 during the Winter, Spring, and Fall quarters in 2026 doing 10 hours of research each week.
Application Link: https://forms.office.com/r/UsqiB7810z
Important Dates:
| November 30, 2025 | Application Submission Opens |
| December 19, 2025 | Application Submission Deadline* |
| January 2, 2026 | Notification of Decision** |
| January 6, 2026 | Confirmation of Participation*** |
| January 9, 2026 | Motorola Solutions Scholars Program Orientation (First Day of the Program) |
| November 24, 2026 | Motorola Solutions Scholars Program Last Day |
* Will continue to receive and evaluate applications until the positions are filled.
**Decision regarding applicant status: whether accepted, not accepted, or pending.
***Once an applicant has been accepted to the program, a response from the applicant, either accept or decline, must be received within one week of the date of the offer letter.
Support for Each Student Participant:
- Mentoring by a designated DePaul Data Science faculty and opportunities to interact with Motorola Solutions professionals
- Research training in an applied field of data science including Artificial Intelligence, Bioinformatics, Medical Informatics, Robotics and Software Engineering.
- Experience in a collaborative research environment.
- Stipend of $5,310.
Program Requirements:
- Full-time undergraduate student with at least 1 year of college/university course work completed as of January 1, 2026.
- Application form.
- Resume.
- Unofficial transcript.
- 2 letters of recommendation.
About recommendation letters:
- It is your responsibility to ask your recommender to send the recommendation letter to aorhean@depaul.edu with the subject line “Motorola Solutions Foundation Scholars Recommendation for Student Name“, where you replace Student Name with your full name.
Course Requirements:
Students should meet both of the criteria below.
- Students should have taken both of the following:
An introduction to problem solving, algorithms and structured programming using a higher-level programming language. The course will focus on skills for developing algorithms, and for writing and debugging programs. Students will learn how and when to use loops, conditionals, and functional abstractions in the context of problems motivated by real world applications.
MAT 130 or above or equivalent or Mathematics Diagnostic test placement into MAT 140 is the prerequisite for this class.
An intermediate course in problem solving, algorithms and programming. Programming skills are further strengthened through more complex and larger programming assignments. The assignments will also be used to introduce different Computer Science areas (e.g. a Client/Server application for the Distributed Systems area). Classes and object oriented programming are motivated and introduced.
CSC 241 is the prerequisite for this class.
- Students should have taken at least one of the following:
A course on computer systems topics, focusing on operating systems components and their relevance for application programming. Caching, memory hierarchy, performance optimization, linking, processes, virtual memory, dynamic memory allocation, system level I/O.
CSC 373 is the prerequisite for this class.
Components of an image processing system and its applications, elements of visual perception, sampling and quantization, image enhancement by histogram equalization, color spaces and transformations, introduction to segmentation (edge detection algorithms), and morphological image processing.
MAT 150 or MAT 262 are prerequisites for this class.
The course is an introduction to the Data Mining (DM) stages and its methodologies. The course provides students with an overview of the relationship between data warehousing and DM, and also covers the differences between database query tools and DM. Possible DM methodologies to be covered in the course include: multiple linear regression, clustering, k-nearest neighbor, decision trees, and multidimensional scaling. These methodologies will be augmented with real world examples from different domains such as marketing, e-commerce, and information systems. If time permits, additional topics may include privacy and security issues in data mining. The emphasis of this course is on methodologies and applications, not on their mathematical foundations.
IT 223 (or MAT 137 or MAT 242 or MAT 341 or MAT 353) is a prerequisite for this class.
Topics include multiple regression and correlation methods, model building and validation processes, analysis of variance, logistic regression and regularized regression techniques.
IT 223 or MAT 351 or MAT 137 is a prerequiste for this course.
(FORMERLY CSC 323) Application of statistical concepts and techniques to a variety of problems in IT areas and other disciplines, using a statistical package for simple data analysis. Course topics include descriptive statistics, elementary probability rules, sampling, distributions, confidence intervals, correlation, regression and hypothesis testing.
MAT 120 or MAT 120 or (MAT 130 or above or equivalent or Mathematics Diagnostic test placement into MAT 140