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 10th 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 10th, 2025 with decisions made within 1 week after the interview.
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
At the discretion of relevant academic units, XJTLU PhD students may be permitted to undertake the following additional responsibilities with appropriate training and guidance:
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
The pay rate for Academic Year 2024-25
How to apply
Applicants should submit their application to AIAC@xjtlu.edu.cn by 10th 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 10th, 2025 with decisions made within 1 week after the interview.