Teaching Assistant

Position Details

Position Information

Position Title Teaching Assistant Position Number 0000000 Hiring Range Minimum $4,000 stipend for 10-week term Hiring Range Maximum $4,000 stipend for 10-week term Location of Position

Remote

Advertisement Text Position Purpose

Online Master of Engineering (MEng) in Computer Engineering Course Teaching Assistants (TAs) will support faculty and students during course run in areas including facilitation of live sessions, holding office hours, monitoring discussion forums, assisting with administration of the Coursera platform, grading of assignments, and providing technical support related to course content for students when necessary. TAs should have demonstrated proficiency in Computer Engineering course content at or above the graduate level.

Required Qualifications

* Bachelor's degree in computer engineering or a related field

* Knowledge of course content for Machine Learning or Signal Processing (depending on position)

* Strong organizational skills, interpersonal and verbal/written communication skills

* Ability to work independently and as a member of the team

* Ability to prioritize, problem solve, multi-task, and effectively manage time to meet deadlines

* Ability to track and follow up on outstanding tasks or matters as needed

Preferred Qualifications

* Master's or doctoral degree in computer engineering or a related field

* Experience as a teaching assistant or course instructor

* Familiarity with learning management systems, particularly Coursera

FLSA Exempt Employment Category Temporary Part time Schedule Department Contact for Recruitment Inquiries Zofia Gajdos Department Contact Phone Number Zofia.

K.Gajdos Department Contact for Cover Letter Thayer Director of Online Education, Zofia Gajdos Contact's Phone Number Equal Opportunity Employer

Dartmouth College is an equal opportunity employer under federal law. We prohibit discrimination on the basis of race, color, religion, sex, age, national origin, sexual orientation, gender identity or expression, disability, veteran status, marital status, or any other legally protected status. Applications are welcome from all.

Background Check

Employment in this position is contingent upon consent to and successful completion of a pre-employment background check, which may include a criminal background check, reference checks, verification of work history, conduct review, and verification of any required academic credentials, licenses, and/or certifications, with results acceptable to Dartmouth College. A criminal conviction will not automatically disqualify an applicant from employment. Background check information will be used in a confidential, non-discriminatory manner consistent with state and federal law.

Special Instructions to Applicants

Dartmouth College has a Tobacco-Free Policy. Smoking and the use of tobacco-based products (including smokeless tobacco) are prohibited in all facilities, grounds, vehicles or other areas owned, operated or occupied by Dartmouth College with no exceptions. For details, please see our policy. (Use the "Apply for this Job" box below).-policy

Additional Instructions

The Thayer School of Engineering at Dartmouth College is seeking teaching assistants (TAs) for courses in the new online Master of Engineering (MEng) in Computer Engineering. Course TAs will support course faculty and students during the course run. Currently we are looking for TAs for Machine Learning with Prof Peter Chin during Spring Term 2024 and for Signal Processing with Prof Kelly Seals during Spring Term 2024.

TA responsibilities will begin in early March and end in mid-June. Spring term courses will be in session from March 25 to June 4, 2024.

TAs should have demonstrated proficiency in the content of the relevant course at the graduate level. The courses are described below:

The Machine Learning course will start with requisite mathematical backgrounds (probability theory, statistics, some basic linear algebra, etc). The course will then cover unsupervised ML models, namely linear regression/classification models, neural network models, and kernel machine models. The course will end with unsupervised…