Capabilities
Four disciplines that are easier to describe separately than they are to practise separately. Most programmes we run draw on at least two of them, and the handover points between them are where the difficult engineering usually lives.
- Medical & biological
Medical and biological systems
Instrumentation, measurement and analysis for clinical and life-science work — from sensing hardware through to the software that makes a signal mean something.
- Biomedical sensing and instrumentation
- Physiological signal acquisition and analysis
- Laboratory automation and data capture
- Device software, validation and traceability
- Electronic
Electronic systems
Mixed-signal and embedded engineering — circuit design, firmware and the measurement discipline that keeps a prototype honest on its way to a product.
- Analogue and mixed-signal circuit design
- Embedded firmware and real-time control
- Low-power and energy-constrained design
- Signal integrity, EMC and design for manufacture
- Communications
Communications
Getting data from where it is produced to where it is needed — radio, networking and protocol work for links that are constrained, contested or simply hard to reach.
- RF and wireless system design
- Digital signal processing and modulation
- Protocol design and network architecture
- Resilient and constrained-link communications
- Artificial intelligence
Artificial intelligence
Machine learning applied where the data is scarce, the physics matters and the answer has to be defensible — including deciding when a model is the wrong tool.
- Applied machine learning on scientific and sensor data
- On-device and edge inference under tight budgets
- Evaluation, uncertainty and failure analysis
- Model governance, provenance and documentation
Most problems arrive without a discipline attached
If you are not sure which of these describes your programme, that is normal — and it is a useful starting point for a conversation.