Mapping the Invisible: Exposomics, Biosensing, and the Future of Health

Mapping the Invisible

Exposomics, Biosensing & the Future of Health

A one-week interactive seasonal school on exposomics, multimodal sensors, digital twins, and solutions-based science.

📅 Sep 14–18, 2026 📍 University of Minnesota 💻 Hybrid

👉 More Info

🗓️ Program Snapshot

DAY 01	Foundations of the Exposome & Study Design
DAY 02	Biosensors & Real-World Exposure Measurement
DAY 03	Advanced Analytics — AI & Multi-Modal Integration
DAY 04	Clinical & Translational Applications
DAY 05	Translation, Solutions & The Path Forward

⚡ Why Attend

How do we measure the invisible? The chemicals around us. The signals our bodies produce in response to exposure. This is the science of exposomics — and it's about to redefine the future of health.

Designed for researchers, clinicians, biomedical engineers, and data scientists, this immersive program brings together internationally recognized experts to explore wearable and various sensors, exposomics, multi-omics technologies, geospatial/data and AI — and how they're transforming precision health.

◆ Hands-on Training

Advanced Bootcamps

Small-group, intensive in-person workshops on: building exposome data pipelines, AI/ML for exposomics, and clinical & data.

⚠️ Only 30 seats per bootcamp — CV + research statement required

◆ Fully Funded

Future Leaders in Exposomics Fellowship

For outstanding graduate students, postdocs & early-career investigators (students get priority). Includes full registration fee waiver plus possible travel support.

📅 Deadline: August 15, 2026

◆ Showcase Your Work

Call for Poster Abstracts

Present ongoing or recent research to leading experts across exposomics, biosensing, AI, and precision health.

📅 Deadline: August 15, 2026

💳 Seasonal School and Bootcamp Pricing

We're Looking for Sponsors 🤝

Put your organization at the center of the exposomics and precision-health movement — in front of the researchers, clinicians, and engineers building its future.

Become a Sponsor →