MATLAB Experts

Nationwide service provider for occupational medicine, occupational safety, and health management with a focus on prevention, health, and digital transformation.

• Role: Responsible for building and further developing professional project portfolio management in the area of Digital Transformation & Data. Steering and supporting strategic IT and transformation projects as well as supporting the introduction and establishment of project management standards, methods, and governance structures.

Focus areas:

• Build-up and further development of IT project portfolio management
• Analysis and structuring of business and technical requirements
• Steering and coordination of cross-functional IT initiatives
• Stakeholder management at business, management, and service provider level
• Introduction and further development of project management methods and tools (including Jira, Confluence, MS Planner)
• Preparation of decision bases, target visions, and implementation concepts in the context of digital transformation
• Steering and coordination of transformation, digitalization, and organizational projects
• Preparation of decision papers and management documents for steering committees
• Running workshops and stakeholder management with business units and IT teams
• Definition of requirements for dashboards, reporting, and KPI steering
• Support in the project Wissenskompass (requirements analysis and MVP concept)
• Analysis and evaluation of AI support options in knowledge management
• Investigation of AI use cases to improve knowledge provision and use
• Consideration of AI requirements as part of the Wissenskompass concept
• Coordination with strategic business units on the further development of project management standards
• Introduction, configuration, and training of Microsoft Planner to support project and task management
• Conducting user training and supporting employees in the introduction of new ways of working and tools

• Led the hardware development and validation of a safety-critical 400 V battery management system (BMS) for the TOGG SUV program; performed system architecture reviews, schematic validations, EMC and reliability tests, and root cause analyses in line with relevant automotive standards (ECE-R10, CISPR25, ISO-26262). Coordinated cross-border EU engineering teams, customer reviews, and technical documentation to deliver a production-ready, validated system.

• Architected and integrated 22 state-of-the-art ADAS validation vehicles for BMW ADCAM and LIDAR programs at Magna Electronics; synchronized up to 26 heterogeneous sensors (LIDAR, RADAR, cameras, GNSS/INS) using PTP-based timing architectures. Defined the system architecture, HW/SW interfaces for hardware-in-the-loop (HIL) reprocessing, sensor integration strategies, and data acquisition frameworks. This enabled scalable vehicle-level validation, improved validation efficiency by 25%, and reduced project costs by €1.45 million.

• End-to-end validation and integration of automotive radar platforms for a Daimler project at Continental. Developed automated open-loop hardware-in-the-loop (HIL) environments to accurately test target tracking KPIs, field of view limits, and thermal and voltage-related ECU state machines. Used a highly complex toolchain consisting of CANoe, Lauterbach Trace32, RADAR target simulators, and EMC shielding chambers for RF and system validation. Synchronized global, interdisciplinary teams to speed up troubleshooting and close critical technical gaps.

• Reconstructed and validated the product architecture of an electromechanical e-bike by integrating and troubleshooting critical subsystems (BMS, motor control, sensors, HMI, electronic locking systems). Built a comprehensive system-level test bench for functional testing, fault reproduction, and performance analysis; coordinated suppliers and implemented corrective actions to improve reliability and traceability.

• Defined the system architecture and validation strategy for a LIDAR platform developed in cooperation with Elmos Semiconductor; evaluated optical measurement concepts, SPAD detector integration, and system requirements, and created technical recommendations for product development and verification.

• Developed LabVIEW-based automation and verification software for a high-precision hydraulic and electromechanical test system for FTE Automotive; integrated NI DAQ hardware for high-frequency real-time capture of physical measurement data. Developed automated test sequences, programmable endurance tests, troubleshooting routines, data logging, and analysis tools to improve test efficiency and traceability.

• Successfully managed complete engineering life cycles for well-known industrial customers (Magna, Continental, Farasis, FTE Automotive, BMW, Daimler, TOGG) – from requirements engineering and proof of concept through system integration and validation to technical documentation, supplier coordination, and user training.

Project goal: Carrying out system tests and validating industrial communication and control systems, including requirements definition and verification.

Responsibilities:

• Test planning, test execution
• Test strategy, test cases, and test specifications
• Requirements analysis
• Ensuring traceability between requirements, test cases, and defects
• Carrying out regression and integration tests
• Defect analysis
• Simulation and validation
• NetSniffer Wireshark
• Supporting test automation (Python, CI/CD)
• Reporting
• Stakeholder coordination and agile collaboration (Scrum / SAFe / Kanban)
• V-Model
• CI/CD automation with Python, Groovy, and frameworks (Selenium, PyTest)

Technologies:: CAN, Modbus, Ethernet, PLC, PROFINET, Codebeamer ALM, Scrum, SAFe, MS Teams, Git, CI/CD with GitLab CI and TeamCity, Windows Batch, FAS, Wireshark, Python, Enterprise Architect (EA), AI tools (e.g. ChatGPT, Microsoft Copilot), VS Code, Tia Portal, SCL, Python (Selenium, PyTest)

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, acceptance, and compliance tests for safety-critical brake control units in the rail industry
• Software test application & integration: Runtime configuration of software components and libraries, validation of interfaces, configuration dependencies, and component interactions
• Test automation (FEAT framework): Co-development and further development of an automated test framework for test execution, reporting, and result analysis
• Functional safety (SiL4, FuSi): Ensuring compliance with safety requirements, traceability and coverage, as well as standards compliance according to EN50126/28/29
• Test automation for communication components: Configuration and validation of fieldbus (CAN) and Ethernet-based TCMS data communication interfaces (TRDP and CIP)
• Requirements analysis & shift-left (PTC Windchill ALM): Analysis of software and system artifacts to identify gaps, ambiguities, and redundancies early in the SDLC
• Test design & test case development: Derivation of test conditions, coverage strategies, and implementation of data-driven test cases (DDT), including reusable test data fixtures
• CI/CD & automation (Python, PowerShell, Jenkins, SVN): Automation of build, test, and HIL deployment processes as well as integration into CI/CD pipelines
• Test data & configuration management (XML): Maintenance and adaptation of XML test vectors and system configurations with automated integration into test environments
• Non-functional testing: Execution of performance and load tests to assess stability and system behavior
• Agile development & defect management (JIRA, Confluence): Participation in Scrum teams, test coordination, review of test artifacts, as well as defect tracking and root-cause analysis
• Error analysis & debugging (CANoe, CANalyzer): Analysis of errors and message flows across multiple system layers (application to bus)
• Model-based analysis (UML, Enterprise Architect): Specification of SUT/SOW and support for systematic test control
• Process & test documentation: Creation of integration and test documentation according to internal quality and certification requirements

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• Responsibility for the further development of digital service products with a focus on customer value, profitability, and scalable growth
• Development and prioritization of the service product roadmap based on customer feedback, market analysis, financial evaluation, and technical feasibility
• Identification of new digital service opportunities as well as derivation of product requirements, feature priorities, and value propositions
• Alignment of product vision, roadmap, and user experience with product owners, center of competence, sales, service, and market organizations
• Support of go-to-market activities, market validations, and internal training for the successful launch of digital service offerings
• Use of data-based decision-making to assess customer needs, business model assumptions, and product progress

Testing and verification of high-voltage systems

• Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
• Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
• Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.

Independent, part-time open-source engineering focused on build-time tooling for Next.js, React, MDX, and JavaScript/TypeScript compiler pipelines.

• Built next-slug-splitter to optimize content-driven Next.js applications. It analyzes MDX content at build time, resolves component usage, and generates route-specific handlers so pages avoid sharing the full catch-all component bundle.

• Created supporting plugins and utilities for scoped MDX transformations, nested component dependency resolution, compile-time refinement, safe ESTree evaluation, and object-graph diffing.

• Own architecture, API design, implementation, automated testing, npm publishing, documentation, demos, and performance benchmarking.

Building blocks:

• remark-scoped-mdx: Context-aware AST transformations with nested scope isolation, typed component registries, and prop inference.

• recma-component-resolver: Dependency-graph analysis and selective component forwarding across nested MDX includes.

• recma-static-refiner: Build-time prop extraction, schema validation, derivation, and pruning.

• estree-util-to-static-value and object-graph-delta: Safe static evaluation and deterministic, cycle-safe structural diffing.

Tech Stack:

• Frameworks: TypeScript · Next.js · React · MDX

• Compiler tooling: Unified · Remark · Recma · MDAST · ESTree · ts-morph · esbuild

• Competencies: Static analysis · AST traversal and transformation · dependency graphs · code generation · schema validation · route and bundle splitting

• Tooling: Vitest · tsup · npm · performance benchmarking

• Engineered and validated a full-vehicle thermal management system in MiL simulation, achieving 95% correlation accuracy against real-world vehicle measurements, directly supporting virtual calibration and reducing dependency on physical test benches.
• Led end-to-end Model-in-the-Loop (MiL) simulation development using AVL CruiseM and MATLAB/Simulink, covering system architecture, parameterization, and validation.
• Acquired and analyzed vehicle sensor measurements (temperature, volumetric flow rate) using dSpace MicroAutoBox (HiL) and IPEmotion, translating raw data into actionable calibration insights.
• Calibrated and optimized critical actuators and thermal components - pumps, valves, electric heaters, heat exchangers, and refrigerant circuits and identified/integrated previously missing physical behaviors to close the gap between simulated and real vehicle performance.
• Designed and tuned an integrated actuator controller with precisely calibrated parameters, producing a high-accuracy virtual model adopted for downstream development use.

• Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
• Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
• Automated extraction and training processes with CI/CD
• Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
• Developed and optimized embedded software for automotive control units
• Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
• Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
• Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
• Developed and trained machine learning models using PyTorch
• Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
• Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
• Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
• Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
• Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

• Developed model-based software for hydrogen tank control units using MATLAB/Simulink, aligned with ISO 26262 functional safety requirements
• Performed system integration and software verification across MIL and HIL environments using dSPACE and Vector tools
• Defined software architecture (SADS) with interface specifications for CAN, analog, and digital communication protocols
• Generated production-ready embedded C code using Simulink Embedded Coder; created and maintained A2L calibration files with CANape
• Designed and executed MIL/HIL test cases ensuring full coverage of functional and safety requirements
• Implemented CODESYS PLC applications on IFM ecomatController; applied CAN and Modbus TCP/IP communication protocols
• Contributed to HMI/UI development using Qt/C++ on Linux (Qt Creator)
• Applied agile Kanban workflow with Git-based version control (Bitbucket)

Tools: MATLAB, Simulink, CANape, CANalyzer, PCAN-View, Embedded Coder, CODESYS, TTC2038/PXROS, Codebeamer, Jira, Git

• Automotive SPICE®: all assessments fully achieved
• Agile transformation: V-model → SAFe successfully implemented
• Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
• Stakeholder management: internal & external
• Process optimization: implemented a continuous improvement process (CIP) with a tracking system

• Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
• Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
• Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
• Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.