Position: Embedded, R&D, IoT developer
Category: C++
English level: upper
Years of experience: 7

My strong points are: problem solving passion,  interdisciplinarity and skills to connect the physical world with artificial, and vise-versa. 
Also, It took a lot of time to develop my own working project management approach, which is adopted for Ukrainian R&D realities (a lot of uncertainties and great risks related to novel technologies development). This PM system based on the publication "ANALYSIS OF RISK AND TIME TO MARKET DURING THE CONCEPTUAL DESIGN OF NEW SYSTEMS", however it wors only in the case if product is realistic by nature. 
My passion for embedded started after my team won the national agrotechnology innovations hackathon.
A few of my projects:
0 - wearable optical sensor for oxygen and water saturation, based on NRF52.
Stack: NRF52 SDK, analog frontend R&D, BLE API developing, testing framework and CI/CD pipeline setup for firmware builds automatic testing.

1 - LoRaWAN temperature monitoring tags for Helium network for the food industry.  I designed and implemented this project from scratch as a pet project, and now it is working, and most parts of the duties have been delegated.
Stack: 
STM32WL + Zephyr OS (STM32Cube on the most recent version), LoRaWAN, AWS cloud services, KiCAD electronics design, product and market research.

2 - Universal device units for building tailor-made case-specific solutions for temperature telemetry, automotive tracking, and similar use-cases based on ESP32. 
Stack: 
ESP32 + ESP-IDF, Arduino core, LoRa, GSM/GPRS/LTE, BLE GAT interfacing API + Java drivers class for Android, system architecture, flexible configuration engine developing, hardware design, production manufacturing setup, IoT dev team leading.
 
3 - Biological data collecting system
In the scope of my PhD thesis, I work on a technology stack that can collect biological data (microalgae growth and health dynamics in different media) in an automated way with the help of a spectral camera, computer vision, and basic spectral analysis. In the early stages, the STM32-based submersible spectral probe was used. On the other hand, this technology was adopted to work with ordinary green plants to analyze the health rates and growth dynamics by leaf morphology and spectral features.
Stack:
STM32 +  Mbed OS (submersible spectral probe))
Python: PlantCV with OpenCV (for morphology data analysis),  Detectron v2 (for morphology data detection), Yolo v5 (for fast RoI detection)
Data analysis: Pandas, Numpy
Cloud integration: Google Drive, Firebase

4 - flexible industrial automation:
The system consists of stm32f303-based CANbus nodes (sensors, actuators, etc). Node-RED includes the visual nodes to make it able to work with hardware over the CANbus adapter.
The system was used to drive 200 solenoid water valves 10 pumps and a couple of other specific units in the experimental water treating system.
Stack:
STM32 + Zephyr OS
Main configuration engine: Node-RED (also I have experience on Total.js Flow engine in the same place), the driving logic also was doubled in Python.
PCB design: Eagle CAD
Currently, I'm interested in military/defense projects.  #MilTech

I would like to work on cutting-edge technology development, preferably in the scopes related to wireless sensors, live/natural sciences, hardware AI / edge computing, or computer vision.