Uni Internship Jan to July 2027 - Computer Vision Model Development for Medical Data Analysis

Synapxe; Singapore; On-site; SGD 13,392 - 26,784; Full time

Job summary

Synapxe in Singapore invites undergraduates to join as an internship in the Data Science & AI team to develop computer vision solutions for healthcare. You will gain practical experience with medical images and videos and contribute to clinical insights through state‑of‑the‑art models.

Phases span review, implementation and evaluation, then adaptive pipelines and model tuning. You will design data preparation, apply transfer learning, and collaborate with senior data scientists to assess

Qualifications

• Undergraduate pursuing a tech-related degree (Year 2 or 3).
• Strong Python programming with clean, well-documented code.
• Solid ML/DL fundamentals: splits, generalization, losses.
• Experience in computer vision workflows is a plus.
• Familiar with PyTorch, TensorFlow, timm, MMDetection.

Responsibilities

• Review literature on CV methods for segmentation, detection and classification in medical data.
• Implement CV models on medical image/video datasets and run baseline experiments.
• Evaluate performance using Dice/IoU, mAP, AUC metrics.
• Design adaptive data pipelines and model tuning strategies.

Skills

Python

NumPy

OpenCV

scikit-learn

PyTorch

TensorFlow

Git

Transfer learning

Computer Vision

Education

Year 2/3 undergraduate

Tools

PyTorch

TensorFlow

MMDetection

OpenCV

scikit-learn

NumPy

Pandas

Git

TIMM

Job description

Join Synapxe as an intern and see how you can contribute in powering a healthier Singapore. Internship@Synapxe is where curiosity meets impact! You would be able to gain practical experience, hone your skills, and be part of meaningful work that improves health through technology!

As an intern you will join the Data Science & AI team to explore and develop advanced computer vision solutions for healthcare applications. This internship offers the opportunity to work with medical images and videos, leveraging state-of-the-art deep learning and computer vision techniques to support clinical insights and decision-making. Interns will collaborate with experienced data scientists and AI engineers to design, evaluate, and optimise computer vision models for real-world healthcare use cases.

Selected Intern(s) Responsibilities

Phase 1 – Review, Implement & Evaluate

• Perform a focused literature review of representative computer vision methods for segmentation, object detection and image classification applicable to medical datasets
• Implement selected computer vision models on medical image and video datasets and conduct baseline experiments
• Evaluate model performance using task-appropriate metrics (e.g. Dice/IoU, mAP, AUC)
• Analyse model results and identify key failure modes and areas for improvement

Phase 2 – Improve Through Adaptive Pipelines and Model Tuning

• Design adaptive data preparation pipelines, including task-specific augmentation, normalisation and dataset splitting strategies
• Experiment with different augmentation schedules and preprocessing approachesApply transfer learning techniques and model tuning strategies to improve baseline performance
• Evaluate different fine-tuning approaches, hyperparameters and model variants
• Integrate interpretability and uncertainty estimation techniques into model workflows
• Assess and compare performance improvements against baseline models

About You

• Undergraduate currently in Year 2 or Year 3, pursuing a degree in Business Analytics, Business Artificial Intelligence Systems, Information Systems, Computer Science, Computer Engineering, Data Science, or a related discipline
• Proficiency in Python programming, with experience writing clean, well-documented code and using common libraries (e.g. NumPy, pandas, OpenCV, scikit-learn)
• Strong foundational understanding of machine learning and deep learning concepts, including training/validation splits, overfitting, optimisation techniques, and common loss functions and evaluation metrics
• Hands‑on experience in computer vision workflows (e.g. image/video preprocessing, augmentation, and model training) is a strong advantage
• Familiarity with deep learning frameworks and libraries (e.g. PyTorch, TensorFlow, timm, MMDetection), including experience applying transfer learning
• Exposure to agentic AI, workflow automation tools, or version control systems (e.g. Git) is a plus
• Independent, fast‑learner, and self‑driven
• Good team player with strong analytical and communication skills
• Ability to multitask and work effectively as part of a multidisciplinary team
• Passionate and keen to make a difference to re‑imagine the future of HealthTech