The Preference Date is the date on which the hiring manager will begin reviewing applications. Those submitted AFTER the preference date will not be considered unless a suitable candidate is not found in the initial screening.
|Position Title:||Intern UCO: Computer Science Data Analyst|
|Department||022010:Assessing Student Transformation|
Intern UCO positions are Student Transformative Learning Record (STLR) funded projects through which students apply academic knowledge gained in their courses to a supervised work situation.
STLR expands students’ perspectives of their relationships with self, others, community, and environment to develop employability, communication, and citizenship skills for success after graduation. STLR documents 5 of the Central 6 tenets for students to showcase to employers, graduate schools, scholarship committees, and others. These tenets are: Global & Cultural Competencies; Health & Wellness; Leadership; Research, Creative & Scholarly Activities; and Service Learning & Civic Engagement. The combination of work experience and academics together strengthens the knowledge base, sharpens these skill sets, and enables the development of real workplace competencies.
The internship is for the Fall and Spring semester and includes professional development meetings through Career Services. This position performs entry level professional duties of moderate difficulty which provides the opportunity for professional development and is responsible for complex project based tasks and industry specific work. This position is for 10 hours per week for the school year.
STLR projects, including Intern UCO, require students to:
|Department Specific Essential Job Functions:||
The past decade has seen rapid development of smart mobile devices, e.g., smartphone, smartwatch, etc., which have opened a new era of mobile computing. All mainstream mobile operating systems, such as iOS, Android, and Windows, have apps for monitoring a person’s activity by counting the number of steps, which is achieved through step detection and step length estimation. Unfortunately, these mobile apps are designed for healthy people. The dynamics of a power wheelchair do not possess the characteristics of steps. Instead, the acceleration and deceleration periods of a wheelchair are short (less than 3 seconds). The subtle changes in maneuvers as well as the sensor and environmental noise make it difficult to determine a wheelchair’s maneuvering status. Particularly, the inertial sensors, such as accelerometers, in a smartphone/smartwatch are very sensitive to noises. Even when the accelerometer is stationary, it still generates sensor readings due to the rotation of the earth, gravity, and/or other environmental noises.
In this study, we aim to develop a generic approach that can effectively reduce noise regardless of the environmental settings. We will employ advanced mathematics and physics methods to process and transform raw accelerometer data (including accelerations in three axes). The transformed data will demonstrate the patterns intrinsic to wheelchair maneuvers, i.e., the data of stationary periods show a similar characteristic, while the stationary and moving data demonstrate distinctive differences. Then, machine-learning techniques can be used to recognize such patterns to accurately determine the wheelchair’s moving status.
Undergraduate student with a sophomore, junior, or senior classification.
Minimum GPA of 2.5.
Attendance at monthly professional development sessions is required. Dates TBA.
Junior, Senior, or Graduate standing.
Computer Science GPA (NOT overall GPA) 3.0 or above.
Has completed CMSC3613 Data Structures and Algorithm Design with at least a B.
Has completed SE 4283/CMSC 5283 Software Engineering I with at least a B.
Solid mathematics foundation.
Excellent communication skills both written and oral.
Repetitive movement of hands and fingers – typing and/or writing. Frequent standing, and/or sitting. Occasional walking, stooping, kneeling or crouching. Reach with hands and arms. Visually identify, observe and assess. Talk and hear.
The noise level in the work environment is usually moderate.
|Salary Range:||$10.00 per hour|
|Desired Start Date:|
|Number of Vacancies:||1|
|Position End Date (if applicable):|
|Eligible for Benefits:||No|
|Grant Effective Date and End Date:|
|Hours Per Week||10|
|Weeks Per Year||52|
|Hours of Work||Varies|
Posting Detail Information
The University of Central Oklahoma (University) is committed to an inclusive educational and employment environment that provides equal opportunity and access to all qualified persons. The University will continue its policy of fair and equal employment and educational practices without discrimination harassment because of actual or perceived race, creed, color, religion, alienage or national origin, genetic information, ancestry, citizenship status, age, disability or handicap, gender, marital status, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law.
For complete details on the University’s EEO policy, please visit the Employee Handbook.
|Job Open Date:||03/20/2017|
|Open Until Filled:||No|
|Special Instructions to Applicants:|
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