Adjunct Instructor - Computer Science

Appointments are made on a term-by-term basis. Adjunct faculty are required to successfully complete an online Canvas LMS orientation course before being scheduled, as well as other training if required by the department. This is an on-site blended/hybrid course, students attend class in person 60% of the time and complete the remaining 40% online through Park’s Canvas Learning Management System.

Local applicants only please.• A background check and government security clearance will be required to get on base.• Click on Park University Locations for more information on our campus centers.• Click on Park University’s Catalog for more information on programs and courses.

To ensure timely submission of your credentials, qualified applicants should apply following the specific instructions noted in each job posting. For all positions, a Masters Degree in the relevant field is required, along with related work and/or academic experience. For most postings, a Ph.D. or other terminal degree is preferred.Park University is an Equal Opportunity Employer encouraging applications from women and minorities. The university will recruit and employ qualified personnel and will provide equal opportunities during employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability or status as a qualified protected veteran. Clickhereto view Park University’s EEOC and related policies.Please Note: Park University participates in E-Verify for all positions at all campus center locations nationwide.

Minimum Qualifications

CS152 Introduction to Python Programming

Master's or Ph.D. level degree in the discipline (CS or IS) or in a related discipline (MBA, etc.) from an accredited school AND received graduate credit with a grade of B or better in the course applied to teach, OR one year Python programming experience outside the classroom.

The Computer Science department requires that you specifically address how professional experience, or graduate coursework pertains to Core Learning Outcome with your Questionnaire submission.

Core Learning Outcomes:

1. Implement a Python program that uses iteration, branching and function calling based on a written problem description.

2. Implement Python functions with parameters.

3. Use lists and strings in a Python program.

4. Access text files for input and output.

5. Define new classes and create corresponding objects.

CS208 Discrete Mathematics

Masters or Ph.D. level degree in the discipline (CS or IS) or in a related discipline (MBA, etc.) from an accredited school AND received graduate credit with a grade of B or better in the course applied to teach, OR one year work experience in subject material of course applied to teach. Satisfactory approval of graduate courses that may include the following discrete math topics and/or names: Discrete Math, Data Structures, Algorithms, Algorithm Analysis, Graph Theory. Alternatively, a PhD in Math, Computer Science, or related technical discipline.

The Computer Science department requires that you specifically address how professional experience, or graduate coursework pertains to Core Learning Outcome with your Questionnaire submission.

Core Learning Outcomes:

1. Implement solutions to problems involving logic, sets, and Boolean algebra.

2. Produce solutions to problems involving recursion and induction.

3. Construct solutions to problems related to counting and probability.

4. Create solutions to problems involving graphs and trees.

Course Description

CS152 Introduction to Python Programming

This course introduces students to computer programming and algorithmic thinking. The current language is Python. Students will learn about variables, data types, input/output, control flow, sequences, and functions.

CS208 Discrete Mathematics

This course introduces the student to selected finite systems pertinent to the study of computer science. Course topics will include combinatorial problem solving, logic, Boolean algebra, combinatorial circuits, sets, relations, functions, proofs, mathematical induction, recurrence relations, graphs, trees, and counting techniques.