Accelerating clinical trial start-up with predictive site selection AI - ON-1121

Project type: Research
Desired discipline(s): Medicine, Life Sciences, Computer science, Mathematical Sciences
Company: Banting AI
Project Length: Flexible
Preferred start date: As soon as possible.
Language requirement: Flexible
Location(s): ON, Canada
No. of positions: 1
Desired education level: Master'sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: Yes

About the company: 

At Banting AI, we’re dedicated to making clinical trials faster, simpler, and more accessible. We use advanced artificial intelligence to streamline clinical research, helping sponsors and sites accelerate timelines, reduce costs, and deliver innovative treatments to patients more effectively. Founded in Canada, Banting AI is committed to improving healthcare outcomes at home and abroad.

Describe the project.: 

Clinical trial initiation remains a major bottleneck, marked by fragmented processes, slow startup timelines, and inconsistent site performance. In Canada specifically, sponsors frequently face difficulties rapidly identifying high-performing research sites, sites encounter operational inefficiencies and unclear trial visibility, and patients experience barriers in accessing suitable clinical trials.

This project aims to address these systemic challenges by developing predictive analytics models leveraging historical operational data, site performance metrics, patient demographics, and Canadian-specific infrastructure elements—including contracting, feasibility assessments, and regulatory approval timelines.

The objectives include:
• Proactively identifying optimal study-site alignments.
• Accelerating trial startup timelines through predictive insights into site performance and operational bottlenecks.
• Improving resource allocation and reducing administrative burdens at clinical research sites.
• Enhancing patient access, especially in underserved, rural, and remote communities, aligning closely with Canadian healthcare equity goals.

Outcomes include:
• Robust predictive AI tools for intelligent and rapid site selection.
• Evidence-based guidelines and best practices for optimizing site initiation processes.
• Tangible improvements in patient accessibility and clinical trial participation.
• Peer-reviewed publications and validated methodologies that are directly scalable beyond Canada to global healthcare markets.

Required expertise/skills: 

• Predictive modeling and statistical analysis.
• Practical experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with clinical trial protocols, operations, and key performance metrics.
• Data management expertise (structured/unstructured clinical datasets).
• Strong collaborative and communication skills.
• Understanding of Canadian healthcare regulations, data governance, and privacy standards (PIPEDA).