Patient support volunteer volunteer volunteer roles in Belfast

The Institute of Physics and Engineering in Medicine (IPEM) is seeking an exceptional individual to join its Board of Trustees as Honorary Secretary, a pivotal governance position at the heart of a respected UK professional body and learned society. As Honorary Secretary, you will contribute at a strategic level, helping to ensure strong governance, supporting effective Board leadership, and shaping the future direction of the charity with real impact across the UK healthcare system.  You will work closely with the President, Trustees and CEO, contributing to strategic decision-making and helping to ensure the Board operates effectively and transparently. 

IPEM operates as a professional body, a learned society, and a registered charity, working across hospitals, universities, and industry. IPEM is supported by a professional and collaborative staff team, working in partnership with its volunteer community. With over 4,700 members working across healthcare, academia and industry, IPEM plays a critical role in advancing physics and engineering in medicine to improve patient outcomes and transforming lives.

This is an unpaid role, with expenses paid for travel and sustenance for in person meetings. We anticipate a time commitment of no more than 12 days per year. It's essential that you are not a member of IPEM and eligible to become a Trustee. This is a 3 year term, renewable once. 

If you would like an informal chat, you can contact IPEM President Mark Knight or CEO Gill Collinson. An information pack and more details about IPEM can be found on our website. To apply for the role, please send your CV and a short cover letter outlining your suitability by 5pm Friday 8th May to Jenny Slack, Operations and Governance Manager.

MednTech

We provide an Android AI tool that analyzes cervical images to support frontline screening decisions.

Synthetic Data Engineer-Medical Imaging

About MedNTech
MednTech is a nonprofit organization developing AI-powered tools to improve early detection of cervical cancer and expand access to care in underserved communities, particularly in Africa. It works by equipping frontline healthcare providers with accessible technology and partnering with local health systems to improve outcomes and empower patients. We have already implemented our diagnostic model in real-world settings in Rwanda. We are now looking to further improve its performance and robustness before our next deployment, and are currently seeking volunteers to support this initiative.

Why Volunteer With Us

We are early-stage, moving fast, and building something that matters. You will have real ownership over technical decisions that directly shape a tool going into the field. Cervical cancer is one of the most preventable cancers in the world, yet it remains a leading cause of cancer death in low-resource settings simply because screening does not reach the people who need it. The tool you help build changes that. If you want your code to save lives, this is that opportunity!

Role Overview
We are looking for a data professional to design and implement synthetic data generation pipelines. These images will be used to augment limited cervical image datasets and improve the robustness of our cervical cancer screening model.

Key Responsibilities

• Develop synthetic image pipelines using diffusion models (Stable Diffusion, DreamBooth, LoRA) or GAN-based approaches
• Generate class-balanced datasets, particularly for underrepresented abnormal cases
• Design and run experiments comparing real vs synthetic vs hybrid datasets
• Evaluate synthetic data quality using FID, KID, and downstream model performance
• Implement prompt engineering and conditioning strategies for medically realistic outputs
• Perform domain gap analysis between real and synthetic data
• Collaborate with ML engineers to integrate synthetic data into training pipelines

Required Skills

• Experience with PyTorch and Hugging Face Diffusers or GAN frameworks
• Strong understanding of data augmentation vs synthetic generation tradeoffs
• Experience evaluating generative models (FID, distribution alignment)
• Familiarity with medical imaging challenges (preferred but not required)

Preferred Qualifications

• Experience with GAN based models and LoRA fine-tuning pipelines

• Prior work with imbalanced or medical datasets

• Understanding of bias in synthetic data

• All roles are highly collaborative and will work closely across the MednTech AI pipeline

• Experience with healthcare AI, low-resource environments, or global health applications is a strong plus

• Candidates should be comfortable working in fast-paced, early-stage environments

Minimum Hours per Week:

4-6 hours per week

Duration:

One-off project

MednTech

We provide an Android AI tool that analyzes cervical images to support frontline screening decisions.

Data Preprocessing Model Specialist-Input Quality & Standardization

About MednTech
MednTech is a nonprofit organization developing AI-powered tools to improve early detection of cervical cancer and expand access to care in underserved communities, particularly in Africa. It works by equipping frontline healthcare providers with accessible technology and partnering with local health systems to improve outcomes and empower patients. We have already implemented our diagnostic model in real-world settings in Rwanda. We are now looking to further improve its performance and robustness before our next deployment, and are currently seeking volunteers to support this initiative.

Why Volunteer With Us

We are early-stage, moving fast, and building something that matters. You will have real ownership over technical decisions that directly shape a tool going into the field. Cervical cancer is one of the most preventable cancers in the world, yet it remains a leading cause of cancer death in low-resource settings simply because screening does not reach the people who need it. The tool you help build changes that. If you want your code to save lives, this is that opportunity!

Role Overview
Your role will be to aid design and implement a preprocessing model that ensures all incoming cervical images meet strict quality and consistency standards before being passed into downstream models.
Our cervical cancer screening method is dependent on pictures taken using a smartphone, leading to large variation in lighting and cervix positions in the image. On top of natural variances in fluid blockages or rotation and angle or cervix, it’s important that only valid images are run through our model.

Key Responsibilities

• Build automated checks for:
• Lighting normalization
• Blur and focus detection
• Angle and framing validation
• Specular reflection handling
• Develop preprocessing models or heuristics to:
• Enhance image quality (contrast, de-noising)
• Standardize image format, resolution, and scale
• Implement image rejection and acceptance criteria pipeline
• Create a quality scoring system for input images
• Collaborate with CV and ML engineers to optimize preprocessing impact on performance
• Optimize preprocessing for real-world noisy data (mobile capture, low-resource settings)

Required Skills

• Strong background in computer vision fundamentals
• Experience with OpenCV, PIL, or similar image processing libraries
• Understanding of image quality metrics
• Experience building automated data validation pipelines

Preferred Qualifications

• Experience with medical image preprocessing

• Familiarity with variability across capture devices and environments

• All roles are highly collaborative and will work closely across the MednTech AI pipeline

• Experience with healthcare AI, low-resource environments, or global health applications is a strong plus

• Candidates should be comfortable working in fast-paced, early-stage environments

Minimum Hours per Week:

4-6 hours per week

Duration:

One-off project