(Available for industry and research positions from July 2026 — based in Bremen, open to relocation).
Hello, I’m Saurabh 👋
I am an embedded software engineer and researcher completing my PhD at the University of Bremen (July 2026). I specialise in runtime fault detection, edge AI deployment, and wireless IoT systems — building microcontroller-based systems that are reliable, intelligent, and power-efficient.
My work sits at the intersection of three areas:
- Real-time embedded software — C/C++, ARM Cortex microcontrollers (ESP32, MSP430, STM32), RIOT-OS, Toit, bare-metal and OS-based firmware
- Applied ML for sensor data — LSTM, Autoencoders, anomaly detection, TinyML, TensorFlow Lite, Edge Impulse, PyTorch, TensorFlow
- Distributed wireless networks — LoRa, BLE, WiFi, multi-protocol sensor node integration
My PhD research at ComNets focuses on enabling microcontroller-based IoT devices to detect and diagnose malfunctions autonomously during runtime — reducing field debugging effort while operating within strict energy and memory constraints. I combine lightweight ML models with embedded instrumentation to bring fault detection and diagnosis capabilities to the edge.
Beyond my core research, I have been involved in MoleNet, an open-platform wireless sensor network developed at ComNets and deployed internationally across Cameroon, Namibia, Sri Lanka, and beyond. I contributed to hardware development discussions including PCB design reviews and power profiling strategies, supervised student projects that used MoleNet prototypes for research, and participated in the broader development activities of the platform — gaining practical experience in taking embedded hardware from board design to real-world field deployment.
Beyond Earth, I contributed to the Humans on Mars Initiative, designing a distributed wireless sensor network for autonomous environmental monitoring in extraterrestrial habitats — a project that pushes the boundaries of reliable IoT under extreme constraints.
I have also fine-tuned a Large Language Model for automated code instrumentation in embedded firmware, and deployed ML inference on resource-constrained hardware using TinyML and TensorFlow Lite.
On this site you will find my publications, projects and other activities.
Technical Stack
| Area | Tools & Technologies |
|---|---|
| Languages | C, C++, Python, Bash, JavaScript |
| Embedded Platforms | ESP32, MSP430, STM32 (ARM Cortex), RaspberryPi |
| Embedded OS | RIOT-OS, Toit, bare-metal, MicroPython, Arduino |
| Communication | LoRa, Bluetooth LE, WiFi, UART, I2C, SPI |
| ML & Edge AI | PyTorch, TensorFlow, TinyML, TF Lite, Edge Impulse, Scikit-learn |
| Debug & Test | JTAG, Logic Analyser, Oscilloscope, HIL Testing, Unit Testing |
| Tools | Git, Docker, CI/CD, ROS, Linux, SQLAlchemy, InfluxDB |
News
📅 2026-05-08
Paper accepted at DCOSS-IoT 2026: “(POSTER) VarDiag: Lightweight and Precise Fault Localization for IoT Systems” — completing the VarLogger/VarDiag framework publication pair. 🎉
📅 2026-02-27
The paper describing my core phd contribution published in ACM TIOT
📅 2025-02-20
Published Reliability Analysis of a Monitoring System for Extraterrestrial Habitats on IEEE Xplore
This work tackles the critical challenge of ensuring reliable monitoring in space missions. It underscores the importance of redundancy and resource-aware strategies in building dependable systems for critical environments.
Paper: Link
📅 2024-12-15
Presented our paper, “Reliability Analysis of a Monitoring System for Extraterrestrial Habitats,” at the main conference of the 20th International Conference on Wireless and Mobile Computing, Networking, and Communications (#WiMob2024) in Paris!
LinkedIn Post: To the post
📅 2024-11-19