Applications are invited for teaching assistant (TA) positions in the School of AI & Advanced Computing at Xi’an Jiaotong-Liverpool University (XJTLU). 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 postgraduate research (PGR) students

• Laboratory demonstration and support for practical work in the classroom
• Attendance on and support with field courses
• One-to-one or group tutoring
• Invigilation of formal examinations and/or class tests
• Delivery of occasional formal lectures 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 departments, the relevant cluster or the Education Development Unit (EDU). Relevant health and safety issues should be covered in this training
• Marking of formative assignments with appropriate training
• Marking of summative assessments with appropriate training. PGR students must never act as the sole examiner on any summative assessment; any work that has been marked by a PGR student MUST be included in the sample provided for internal and external moderation.

XJTLU masters students, and graduate students (postgraduate research and masters) from other universities

• Tutoring or lab demonstration
• Scheduled office hours for one-to-one tutoring
• Assistance in marking of formative assignments, reports, quizzes, etc. with appropriate training and guidance. For marking of any assessment that contributes to the final module mark, the Teaching Assistant may only perform mechanical marking tasks as supervised by the Module Examiner, not marking tasks that involve academic judgment.

Skills and Knowledge

DTS002TC Essentials of Big Data
Credits: 5
Delivery Mode: Lectures: 2*7, Seminars:2*7 ,
Labs: 4*7; Second Block
Number of TA needed: 8
Requirements: Proficient with Matlab

DTS101TC Introduction to Neural Networks
Credits: 2.5
Delivery Mode: Lectures: 4*5,Seminars: 2*1 ,
Tutorials: 2*2; First block
Number of TA needed: 1
Requirements: Strong experience in deep
learning, machine learning, and Python

DTS104TC Numerical Methods
Credits: 2.5
Delivery Mode: Lectures: 4*5, Seminars: 2*1 ,
Tutorials: 2*6;Second block
Number of TA needed: 2
Requirements: Good mathematical background and
experience with Matlab

CPT103TC Introduction to Databases
Credits: 5
Delivery Mode: Lectures: 5*5, Seminars: 2*1,
Labs: 5*5; Second block
Number of TA needed: 3 to 4
Requirements: Good knowledge of database design
and SQL

DTS203TC Design and Analysis of Algorithms
Credits: 5
Delivery Mode: Lecturers:8*5, Seminar：2*1, Labs: 1*6;First block
Number of TA needed: 2
Requirements: (1) knowledge of algorithms
(2) be able to implement algorithms with Python

DTS204TC Data Visualisation
Credits: 2.5
Delivery Mode: Lectures: 4*5, Seminars:2*1;
Labs: 1*6;Second block
Number of TA needed: 2
Requirements: Be familiar with or interested in
Data Visualisation. Skills: JavaScript,D3.js

DTS205TC High performance computing
Credits: 2.5
Delivery Mode: Lectures: 3*5, Seminars:2*1;Tutorial:
1*6, Labs: 1*6;First block
Number of TA needed: 2
Requirements: Familiar with Java if the
candidate is a PhD student, if familiar with both Java and Hadoop, master level
is fin.

DTS206TC Applied Linear Statistical Models
Credits: 5
Delivery Mode: Lectures: 8*5, Seminars:2*1 ,
Tutorials: 1*6 Second block
Number of TA needed: 2
Requirements: Proficient in R

Pay rate

• 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 Ruimei.Qiu@xjtlu.edu.cn by 29th Jan, 2022

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

The interview is expected to be arranged around Feb 15th, 2022 with decisions made within 1 week after the interview.