Teaching Assistant

Teaching Assistants in the School of AI & Advanced Computing

Applications are invited for teaching assistant (TA) positions in the School of AI & Advanced Computing at XJTLU Entrepreneur College (Taicang). The TA positions are open for all qualified interested graduate students (Masters and PhD students) on campus of XJTLU or other graduates in Suzhou. All applications will be considered by the recruiting panel which consists of two or more academic staffs in the Department.

TA duties include:

XJTLU Master’s students, XJTLU PhD students, and postgraduate (Master’s and PhD) students from other universities

• Laboratory demonstration and support for practical work in the classroom
• Attendance on and support with field courses
• Group tutoring
• Scheduled office hours for one-to-one tutoring
• Invigilation of formal examinations and/or class tests
• Marking of formative assignments with appropriate training
• Other appropriate activities as determined by academic units

At the discretion of relevant academic units, XJTLU PhD students may be permitted to undertake the following additional responsibilities with appropriate training and guidance:

• Delivery of occasional formal lectures and/or seminars within their area of expertise after having received appropriate training and initial supervision from their supervisor or the respective Module Leader. Such training might be provided by individual academic units, or the Education Development Unit (EDU). Relevant health and safety issues should be covered in this training.
• Assistance in marking of summative assessments with appropriate training and under the supervision of the module leader, subject to prior approval from the Head of the academic unit delivering the module. However, they must never act as the sole examiner on any summative assessment. All assessment tasks marked by XJTLU PhD students must be moderated, and they are not permitted to act as moderators.

Modules include

DTS002TC     Essentials of Big Data (Location: SIP)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; Second block

Number of TA needed: 10-20

Requirements: Proficient with Python

DTS101TC     Introduction to Neural Networks (Location: TC)

Credits: 2.5

Delivery Mode: Lectures: 2*5, Seminars: 2*1, Labs: 2*5; First block

Number of TA needed: 6

Requirements: be familiar with Jupyter Notebook, Neural network and Deep learning, anaconda, Tensorflow, and PyTorch (beginner level)

DTS103TC     Design and Analysis of Algorithms (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; First block

Number of TA needed: 10

Requirements: Familiar with Algorithms and Python Programming.

DTS104TC     Numerical Methods  (Location: TC)

Credits: 2.5

Delivery Mode: Lectures: 2*5, Seminars: 2*1, Labs: 2*5; Second block

Number of TA needed: 8-16

Requirements: Having knowledge of numerical methods and skills in MATLAB coding.

DTS106TC     Introduction to Databases   (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; Second block

Number of TA needed: 9

Requirements: Good knowledge of database design and SQL

DTS203TC     Design and Analysis of Algorithms (Location: TC)

Credits: 5

Delivery Mode: Lecturers: 2*2*5, Seminar：2*1, Labs: 2*2*5; First block

Number of TA needed: 4

Requirements: (1) knowledge of algorithms (2) be able to implement algorithms with Python

DTS208TC     Data Analytics and Visualisation (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1; Labs: 2*2*5; Second block

Number of TA needed: 5

Requirements: have the ability to process data using Python.

DTS206TC     Applied Linear Statistical Models (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; Second block

Number of TA needed: 5

Requirements: Proficient in R

DTS304TC   Machine Learning (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; First block

Number of TA needed: 8

Requirements: 1. Proficiency in traditional machine learning techniques and popular deep learning models.2. Good knowledge of Python, PyTorch, and scikit-learn.

DTS307TC   Reinforcement Learning (Location: TC)

Credits: 5

Delivery Mode: Lectures: 2*2*5, Seminars: 2*1, Labs: 2*2*5; Second block

Number of TA needed: 8

Requirements: 1. Python programming skill, familiar with deep learning related libraries such as pytorch, matplotlib, tensorboard, etc. 2. Reinforcement learning basics.

Skills and Knowledge

• Proficient spoken and written English
• Excellent communication and interpersonal skills
• Excellent presentations skills, and skillful in Microsoft Office Software (Word, Excel, PowerPoint, etc.)
• Excellent organizational skills and attention to detail
• Be adaptable and open to change
• Previous TA working experience preferred

The pay rate for Academic Year 2024-25

• XJTLU PhD students: RMB 60/hour (before tax)
• XJTLU Master’s students: RMB 50/hour (before tax)
• PhD and Master’s students from other universities: RMB 50/hour (before tax)

How to apply

Applicants should submit their application to AIAC@xjtlu.edu.cn by 10^th Feb, 2025

Applicants are required to provide their CVs and academic transcripts of previous studies.

For XJTLU students, please apply for TA positions through the Teaching Assistant Management System(TAMS) https://ta.xjtlu.edu.cn directly. The interview is expected to be arranged around Feb 10^th, 2025 with decisions made within 1 week after the interview.