Control Systems Specialist

Designed and implemented a cloud-based real-time telemetry system monitoring a fleet of process plants. Sensor readings, pressures, flow rates, and alarm events are collected from each system via compressed MQTT streams, processed by a Python ingestion service, and stored in a TimescaleDB time-series database with automated multi-resolution aggregation, compression, and retention policies. On-site collector services read live data directly from the plant controllers, and a compression bridge reduces uplink bandwidth by more than 90% on metered connections. Operational metrics and alarms are visualised through custom Grafana dashboards in a web browser. Cloud infrastructure sizing was derived from measured production data, achieving a sustained compression ratio of 40–53× on stored time-series data. The daily flow of data points is in the range of 3 millions.

Developed a service running on the plant PC that reads live process data from the Beckhoff controller, computes plant status and inventory, and reports to an external partner API, replacing a defective OEM component. Delivered with AI-assisted development under close engineering direction and validated against physical measurements before going live. In production across multiple sites, deployed and updated remotely.

Designed and built a HIL test bench for a Danish climate-control company: a real-time thermal simulation of a radiator, room, and building envelope drives a temperature-controlled cabinet, so real devices are exercised against simulated buildings. Includes a web dashboard, time-series storage, and automated, repeatable test campaigns delivering client-ready comparison reports.

Extended an existing C++17-based wind turbine simulator with a new component for synthezising grid voltage, enabling simulation of a multitude of operational and failure scenarios. Designed and implemented an API with a Python counterpart for automated testing.

Using the Plecs electrical simulation tool, Fault-Ride-Through (LVRT/HVRT) scenarios were tested and validated on an existing model adapted for the specific requirements.

Designed web-based user interfaces for multiple hardware-in-loop test systems, including real-time controls and data visualisation. Delivered rapid prototypes leveraging AI-assisted development.

In a large-scale HIL system with multiple network routers, designed the network layout and created a Python-based tool for deploying configurations. Component archetypes defined in YAML were converted into RouterOS commands for automated router provisioning.

Developed a web-based user interface for an automotive device controlled via CAN-bus on an embedded Linux system. Implemented data logging to Azure IoT Hub with downstream storage in PostgreSQL and created Grafana dashboards for device state and performance monitoring. Coordinated external subcontractors for cabling and enclosure design.

Key figure in the inception of the product concept, focusing on wireless connectivity, usability, and control design. Introduced Model-Based Design to the heating segment. Part of a 3-person software team, focusing on heating control and data collection for a Cypress PSOC4 ARM Cortex-M0 target. Led control performance certification by a Notified Body. The product received multiple awards and has surpassed 1 million units sold.

Leveraged a large dataset to train a neural network, which was then deployed on an existing low-cost, battery-powered embedded platform. Evaluation was performed using Matlab and Python against real-world datasets. Implemented using the Deep Learning Toolbox.