Senior Research Engineer – Control and System Dynamics bei ENODA
ENODA · Edinburgh, Vereinigtes Königreich · Onsite
- Senior
- Optionales Büro in Edinburgh
We have an exciting opportunity for an experienced Senior Research Engineer (Control and System Dynamics) to join our business as we continue to scale.
We are seeking a Research Engineer to lead the experimental characterisation and control performance analysis of ENODA’s Prime Exchanger technology. This role focuses on understanding and reducing control latency, validating dynamic response under realistic operating conditions, and developing models that bridge laboratory testing with real-world deployment. The candidate will collaborate with colleagues working on high-fidelity digital twin development, but will be primarily responsible for experimental validation, control optimisation, and applied modelling.
This role would suit a candidate who has worked at the intersection of academic research and industrial R&D, with proven experience in both advanced experimentation and modelling. The successful applicant will combine strong analytical skills with hands-on laboratory expertise, and will thrive in a multidisciplinary environment bridging hardware, firmware, and data science.
Who we are
ENODA’s world-class team are building the first truly decentralized, intelligent energy system that will reshape how we generate, store, and distribute energy.
Foundational to our holistic and integrated platform approach, the Enoda PRIME® Exchanger, is first-of-a-kind dynamic power flow hardware designed to maintain “on-target” frequency and voltage, increasing grid efficiency and capacity for distributed energy sources like renewables and EVs.
Enoda ENSEMBLE™ is our ecosystem co-ordinator of ENODA and third-party assets, able to forecast, aggregate, and bid capacity in real time, functioning as a networked “operating system” for the electrical grid.
This is more than simply managing electricity flow. It is about creating the foundational system to prepare for a radically more capable, prosperous, and intelligent future.
Check out the ENODA YouTube channel to learn more about us.
You'll spend your time:
Experimental Validation:
- Designing and executing laboratory experiments (scaled and full power) to measure transient response, load step behaviour, and short-circuit dynamics.
- Leading hardware-in-the-loop (HIL) campaigns with real controllers under simulated grid conditions.
Control Performance Analysis:
- Investigating and quantifying latency sources in the control loop (sensing, computation, actuation, magnetic dynamics).
- Proposing and prototyping strategies to reduce response times from multi-cycle to sub-cycle levels.
System Modelling:
- Developing reduced-order and control-oriented models of system dynamics to guide controller design and validation.
- Collaborating with digital twin specialists to align experimental findings with simulation environments.
Data Generation:
- Building internal datasets of operational behaviour (thousands of hours) from controlled experiments to benchmark and improve stability, efficiency, and robustness.
Cross-functional Collaboration:
- Working with hardware, firmware, and data science teams to ensure experimental insights translate into practical design improvements and field-ready control strategies.
The key experience we're looking for:
Power Electronics & Control:
- Deep understanding of converters, inverters, and grid-interactive control schemes.
Power Transformer Design:
- Experience with short-circuit ratio analysis and weak-grid stability.
Experimental & Lab Skills:
- Proven ability to design and execute experiments on power electronics hardware.
- Skilled in the use of oscilloscopes, power analysers, and HIL platforms (dSPACE, OPAL-RT).
- Capable of designing safe setups for transient and fault testing.
Simulation & Modelling:
- Proficiency in real-time simulation tools (Simulink/PLECS, PSCAD, RTDS, OPAL-RT).
- Strong coding ability in Python or MATLAB for control analysis and reduced-order modelling.
- Familiarity with digital twin approaches is desirable but not the primary focus.
Data-Driven Methods:
- Knowledge of time-series analysis, signal processing, and machine learning methods for system identification and forecasting.
The following would be advantageous:
- Experience with model predictive control (MPC) or reinforcement learning applied to power converters.
- Background in system identification for complex energy or industrial systems.
- Familiarity with thermal modelling and loss estimation in magnetic/electronic systems.
- Prior experience with grid compliance testing (fault ride-through, harmonics, stability standards).
- Familiarity with digital twin approaches.
What we offer:
- Salary: circa £80K - £100K dependent on skills & experience
- Private Medical Insurance
- Flexible Pension policy tailored to your requirements
- 33 days annual leave + Holiday purchase/sell scheme
- Dental cash-back scheme
- Cycle-to-Work scheme
- Season travel ticket loan
- Enhanced company sick pay & Income Protection
- Life Assurance
This role is based onsite at Quartermile in Edinburgh.
