Under the guidance of Dr Jipsen, Dr Gil-Ferez and Dr Kurz, work on a literature review on neurosymbolic AI, underpinning a planned course on Trusted AI, and to implement a proof-of-concept software serving as the basis for future projects Required Qualifications Senior undergraduate level of experience with software engineering, in particular in the area of machine learning and in the area of programming languages and formal methods. Desired Qualifications Knowledge in one of the following areas... more details
Student Assistant - Trusted AI Training Program
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Posting Details
Student Title Classification Information
Quick Link
https://chapman.peopleadmin.com/postings/34436
Job Number
SE41724
Position Information
Department or Unit Name
Fowler School of Engineering
Position Headcount
1
Position Title
Student Assistant - Trusted AI Training Program
Academic Year
Summer 2024
Term or Semester
Summer
Is this Role for an Undergrad or Grad Student?
Grad
Anticipated Pay Range
$25.00 - $25.00
Pay Range Information
Chapman University is required to provide a reasonable estimate of the compensation range for this position. This range takes into account a variety of factors that are considered in making compensation decisions, including experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. Salary offers are determined based on the final candidate’s qualifications and experience, as well as internal equity and other internal factors. The anticipated pay range is not a promise of a particular wage.
On which Campus will this work be done?
Orange
Approved Supervisor
Alexander Kurz
Supervisor Email
akurz@chapman.edu
Scheduling
Please use dates within Chapman Academic Calendar
(https://www.chapman.edu/academics/academic-calendar.aspx)
Desired Start Date
06/01/2024
Projected End Date
05/25/2025
Average number of hours per week
Up to 10 Hrs
Position Summary Information
Job Description Summary
To address the need for trustable and interpretable algorithms, this project is part of the Training Program in Trusted Artificial Intelligence and centers on bridging the gap between neural and symbolic AI through the use of controlled natural languages.
Responsibilities
Under the guidance of Dr Jipsen, Dr Gil-Ferez and Dr Kurz, work on a literature review on neurosymbolic AI, underpinning a planned course on Trusted AI, and to implement a proof-of-concept software serving as the basis for future projects
Required Qualifications
Senior undergraduate level of experience with software engineering, in particular in the area of machine learning and in the area of programming languages and formal methods.
Desired Qualifications
Knowledge in one of the following areas: symbolic regression in machine learning, knowledge of the Lean proof assistant
Special Instructions to Applicants
Budget Information
Is Federal work-study required?
With or Without FWS
Pre-screening Questions
Required fields are indicated with an asterisk (*).
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