- Bachelor's degree: Mechatronics, Brno University of Technology, 2021-2024.
- Master's degree: Applied Informatics, Brno University of Technology, 2024-2026.
- Embedded systems,
- Machine Learning, Deep Learning, Data Science.
Embedded Software Engineer
Bender Robotics. May 2023 — December 2024.
- Firmware development for STM32 Cortex M in compliance with MISRA C.
- Development under FreeRTOS, HAL and Bare Metal.
- Build system setup using CMake, Makefile.
- Peripheral integration: ADC, DAC, UART, USB, I2C and SPI.
- Communication systems using HDLC and Protocol Buffers.
- Unit testing using Unity Test Framework.
- Code versioning and collaboration via GitLab.
- Maintained a CI/CD pipeline with linters and static analysis tools.
Key Achievement:
- Fully designed and implemented from scratch a modular firmware and Python PC API for the company's Touchscreen Tester device.
- Automated touchscreen testing on the production line, reducing manual effort and improving test consistency.
Variant Management Tool Developer
NXP Semiconductors. February 2025 — Present.
- R&D in Product Line Engineering following the V-model development process.
- Development of Feature Models and Variants using pure::variants and Eclipse.
- Integration of IBM DOORS Next and IBM ELM RM to automate the flow of requirements into variant models.
- Automation of configurations via pure::variants JavaScript API, Eclipse console, CMake, Makefile and Python.
- Task and code management using Jira and Bitbucket.
Maze Pathfinding with Search Algorithms – Graph Search and Route Planning - Implemented classic pathfinding algorithms (BFS, DFS, A* and Jump Point Search) to solve mazes represented as grids. Implemented and Used efficient data structures: Double-Linked List for stacks/queues and Binary Heap for priority queues. Visualized results and stored paths and performance metrics using Pandas and image exports using Pillow.
File Compression with Canonical Huffman Code – Data Compression Algorithm - Implemented Canonical Huffman coding in C for compressing and decompressing ASCII files. Built frequency table and Huffman tree using a min-heap implemented from scratch, generated Canonical Huffman codes, and saved metadata for decompression. Developed a command-line argument parser to handle compression and decompression options with input/output file paths. Compiled the project using Makefile and processed files in chunks to handle large data efficiently.
TSP Solver – Metaheuristic Algorithms for the Traveling Salesman Problem - Implemented Genetic Algorithm and Simulated Annealing in C++ for solving the TSP. Applied Object-Oriented Programming and the Strategy Design Pattern to enable modular algorithm selection via a common interface. In Simulated Annealing, used swap for neighbor generation at high temperatures and 2-opt at lower temperatures for better refinement. Designed a flexible architecture with a TSPContext class managing solver strategies and an abstract TSPSolver base class. Supported specific configurable runs, file-based input/output, and built the project with a Makefile.
Machine Learning algorithms implemented from scratch: Linear Regression, Logistic Regression, Support Vector Machine, k-Nearest Neighbors.
Function Approximation and Deployment with Neural Network – Regression - Trained a neural network to approximate a mathematical function using synthetic data. Saved the model and preprocessing parameters in JSON for loading model. Deployed the model with FastAPI and Uvicorn, exposing a REST API for predictions. Evaluated the model's performance using MSE, MAE and R² metrics.
Data Classification with Neural Network - Classification - Built and trained a binary classifier using Keras on a medical dataset. Saved the model and preprocessing parameters to JSON for loading and using pretrained model for inference. Evaluated the model's performance using Accuracy, Precision, Recall, F2Score and Precision-Recall Curve metrics.