New opportunity

Research Associate (Computer Vision and Artificial Intelligence)

Nanyang Technological University · Singapore

About this role

Nanyang Technological University seeks a Research Associate for the “Manual Assembly Job Quality Inspection” project at its main campus in Singapore. The research focuses on developing AI algorithms for object and defect detection.

Responsibilities

Research and develop AI technologies for object and defect detection, and integrate the resulting algorithms into a system.
Oversee the project budget and coordinate equipment procurement and maintenance.
Mentor and provide academic guidance to students, and prepare project reports.

Requirements

Master’s degree in Computer Science, Electrical and Electronic Engineering, or a related field, plus a minimum of 1 year of relevant experience in computer vision, artificial intelligence, or a related area.
Proficiency in C and Python and in deep learning frameworks such as PyTorch and TensorFlow; knowledge of imaging and computing devices and equipment.
Strong communication, organizational, and time-management skills, with the ability to work independently and as part of a team.

Preferred

Prior publication experience. This is a full-time position.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Computer visionArtificial intelligenceObject detectionDefect detectionDeep learningCPythonPyTorchTensorFlowSystem integrationImaging equipmentProject budgetingEquipment procurementResearch reportingStudent mentorshipCommunicationOrganizationTime managementTeamwork

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Object and defect detection research
The project focuses on developing AI algorithms for object and defect detection in manual assembly job quality inspection.
System integration
The role includes integrating the developed algorithms into a system. The posting also calls for C and Python proficiency, deep-learning frameworks such as PyTorch and TensorFlow, and knowledge of imaging and computing equipment.
Project resources
The associate oversees the project budget and coordinates equipment procurement and maintenance.
Student guidance and reporting
The role provides mentorship and academic guidance to students and prepares and compiles project reports.

Likely challenges to think through

Judging detection quality
Preparation hypothesis: an algorithm that detects objects well may not detect defects equally well, especially if examples are limited or visually subtle. Be ready to explain how you would evaluate both tasks, inspect errors and decide what to improve.
Moving from a model to a system
Preparation hypothesis: model quality may need to be balanced against imaging and computing constraints during integration. Prepare to discuss how you would test an algorithm with the available devices and diagnose a gap between research results and system behavior.
Balancing research and project duties
Preparation hypothesis: algorithm development could compete for time with procurement, maintenance, student guidance and reports. Prepare an example of how you would track dependencies, communicate progress and protect time for experiments without neglecting those duties.

Practice your answers

Technical practice

How would you define object detection and defect detection for a manual assembly inspection task?

What to coverThe project focuses on AI algorithms for both object and defect detection. Suggested preparation: explain how you would define the target outputs and distinguish a missing or misplaced object from a defect, while stating any assumptions about the inspection task.

How would you build and split an image dataset for this research?

What to coverThe role involves developing detection algorithms, but the posting does not specify a dataset. Suggested preparation: discuss annotation consistency, coverage of normal and defective examples, and splits that avoid leakage between training and evaluation.

How would you choose an initial detection approach?

What to coverThe posting calls for research and development of AI technologies for object and defect detection. Suggested preparation: compare a simple baseline with a deep-learning approach, considering data availability, detection accuracy, computational cost and ease of integration.

How would you evaluate whether a defect detector is useful?

What to coverDefect detection is a stated project goal; no success metric is specified. Suggested preparation: explain precision and recall, the consequences of missed defects versus false alarms, and how you would examine performance across different defect types.

How would you investigate a detector that performs well in testing but poorly on new assembly images?

What to coverThe role covers algorithm development and system integration. Suggested preparation: check for changes in lighting, viewpoint or image quality, as well as annotation issues and train-test leakage, before deciding whether to adjust data, imaging or the model.

What imaging and computing equipment considerations matter for an inspection system?

What to coverThe posting asks for knowledge of imaging and computing devices and equipment. Suggested preparation: discuss how image resolution, lighting, camera placement, processing capacity and maintenance can affect detection reliability, without assuming particular equipment is used.

How would you organize a detection experiment in PyTorch or TensorFlow?

What to coverThe posting names PyTorch and TensorFlow among relevant deep-learning frameworks. Suggested preparation: explain a reproducible workflow for data loading, training, validation, checkpointing and comparison against a baseline; do not assume the project mandates one framework.

How would you use Python and C when moving a detection algorithm into a system?

What to coverThe role calls for proficiency in C and Python and integration of developed algorithms. Suggested preparation: describe possible language boundaries, data exchange and testing considerations, while making clear that the posting does not prescribe an architecture.

How would you test an integrated detection system beyond model accuracy?

What to coverSystem integration is an explicit responsibility. Suggested preparation: describe checks for image-input handling, output consistency, processing time and failure cases, and explain how you would distinguish model faults from integration faults.

How would you present research results in a project report?

What to coverThe role includes preparing project reports, and prior publication experience is preferred. Suggested preparation: outline how to document methods, datasets, metrics, limitations and next experiments so that results can be reviewed and reproduced.

Behavioural practice

Tell me about a time you advanced research while working independently and with a team.

What to coverThe posting calls for the ability to work both independently and in a team. Suggested preparation: give a real situation, your own actions and the result, distinguishing independent decisions from contributions made with others.

Describe a time you guided a student or helped someone learn a technical concept.

What to coverMentorship and academic guidance to students are stated responsibilities. Suggested preparation: use a real example to explain the learner's need, how you adapted your guidance and what resulted; if you have not mentored a student, identify the example's different context.

Tell me about a time you coordinated equipment needs within a budget or other resource constraint.

What to coverThe role includes overseeing a project budget and coordinating equipment procurement and maintenance. Suggested preparation: describe the situation, how you prioritized needs and coordinated action, and the outcome, without claiming experience you do not have.

Describe a time you managed competing technical tasks and reporting deadlines.

What to coverThe posting names organizational and time-management skills and responsibility for project reports. Suggested preparation: give a real situation, your prioritization and communication actions, and the result, including any trade-offs you made.

Tell me about a time you communicated a technical finding or limitation to collaborators.

What to coverStrong communication skills are required, and the role includes compiling project reports. Suggested preparation: explain the audience and situation, how you made the finding and its uncertainty clear, and what action or decision followed.

Sharpen your knowledge

Topics to prepare

Object and defect detection
The Manual Assembly Job Quality Inspection project focuses on AI algorithms for both. Revision exercise: explain how you would define each detection task and distinguish a missing or misplaced object from a defect; these examples are preparation suggestions, not specified project categories.
Deep learning for computer vision
The posting asks for computer vision and AI experience and names PyTorch and TensorFlow. Revision exercise: compare how you would train, validate and diagnose a detection model in the two frameworks. The posting does not prescribe a model architecture.
Imaging and computing equipment
Knowledge of imaging and computing devices and equipment is required. Revision exercise: explain how image quality and computing capacity could affect detection results, then identify what equipment details you would need before designing an experiment.
Algorithm-to-system integration
Integrating developed algorithms into a system is an explicit responsibility. Revision exercise: sketch the path from an image input through a detector to a usable output, marking where you would check errors. The posting does not specify an integration platform.

Skills to revise

Implement a detection experiment
The role calls for AI algorithm development and proficiency in Python and deep learning frameworks such as PyTorch and TensorFlow. Practice writing a small Python training-and-inference example in one named framework and explaining how you would assess its object or defect detections.
Explain C and Python code choices
C and Python are named programming languages in the requirements. Practice describing a small image-processing routine in each language and the trade-offs you would consider when integrating it with a detection algorithm; this division of work is a preparation suggestion, not a stated project design.
Plan equipment and budget work
The associate oversees the project budget and coordinates equipment procurement and maintenance. Practice drafting a brief equipment-needs list with estimated costs, purchasing priorities and a maintenance check; no budget amount or purchasing process is specified.
Report and guide research work
Preparing project reports and mentoring students are explicit responsibilities. Practice a one-page account of a detection experiment—aim, result, limitation and next step—then explain it as guidance to a student. If relevant, prepare to discuss publication work; prior publication experience is preferred, not required.

Your preparation checklist

Map evidence to the stated requirements
Prepare a one-page summary of your degree, relevant experience, and examples using C, Python, and deep-learning frameworks such as PyTorch or TensorFlow. Note what you can demonstrate rather than assuming a match.
Build an object- or defect-detection walkthrough
Select a relevant project, if available, and prepare a short account of the problem, data, model choices, evaluation results, failure cases, and your contribution. The advertised project focuses on manual assembly job quality inspection.
Practice an inspection-system design exercise
As interview practice, sketch how you would approach image capture, object and defect detection, evaluation, and algorithm integration for manual assembly inspection. Label assumptions and trade-offs; the posting does not specify an implementation.
Explain imaging and integration decisions
Prepare an example or diagram showing how an algorithm connects to imaging and computing equipment, including testing and troubleshooting decisions. This addresses the stated equipment-knowledge and system-integration responsibilities.
Prepare a project-operations example
Outline how you would track a research budget and coordinate equipment procurement and maintenance. Use an actual example if you have one; otherwise present it as a proposed approach.
Gather research communication examples
Prepare a sample report outline and an example of mentoring or guiding a student, if available. If you have publications, be ready to explain your contribution; publication experience is preferred, not required.
Write questions for the research team
Bring focused questions about available inspection data, how detection performance will be assessed, the equipment and integration environment, and expectations for reports and student guidance. These are points to clarify, not details supplied by the posting.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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