10–12 August 2027Oxford + online

Biomedical AI

Health, Biology & Discovery

Connect clinical and physiological data with genomics, proteins and molecular design. Explore how predictions and generative models inform therapeutic discovery and experimental validation.

/Four modules across human health, biology and discovery

I · Human Health

Clinical & Physiological Systems

Models of the patient across care, time and everyday life.

Topics

Medical imaging
EHR & patient trajectories
Wearables & biosignals
Clinical multimodality
Risk, calibration & utility

II · Biology

Sequences, Structure & Function

Learning across biological sequences, structures and scales.

Topics

Genomics & multi-omics
Proteins & proteomics
Structure and function
Translation across scales

III · Discovery

Design, Prediction & Experimentation

Designing therapeutic candidates and connecting predictions to experimental evidence.

Topics

Molecular representations
Property & biological activity prediction
Generative molecular design
Drug discovery & candidate optimisation
Autonomous experimentation
Scientific validity & experimental validation

IV · Labs

Hands-On Cases

Three practical sessions for applying AI methods across health, biology and discovery.

Coming soon

Lab topics will be announced.

Who it’s for

Researchers, engineers and scientific leaders

AI researchers and engineers, computational biologists, and scientific leaders building systems for medicine, biology and therapeutic discovery.

What you’ll gain

From biological data to validated discovery

Understand the distinct data structures of clinical and biological systems.
Connect representations and architectures to prediction and generative design.
Evaluate reliability, clinical usefulness and scientific validity in context.
Connect molecular discovery with autonomous experimentation and experimental validation.

/OxML 2027 registration

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From biological data to validated discovery

Understand the distinct data structures of clinical and biological systems.

Connect representations and architectures to prediction and generative design.

Evaluate reliability, clinical usefulness and scientific validity in context.

Connect molecular discovery with autonomous experimentation and experimental validation.