MATLAB Expert

Development of AI and traditional models to analyze medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, referral profile analyses, and prescription analyses. Analyses in the field of Real World Evidence.

Healthcare IT, pharmaceutical industry

Tasks, responsibilities:

• Gathering customer requirements
• Planning the subproject
• Designing and defining KPIs
• Designing and developing models and visualizations of results using customer dashboards
• Developing and implementing data warehouse adaptations
• Deriving recommendations for action

Methods, technologies:

• Simulation
• Artificial intelligence
• Python
• R
• SQL
• Microsoft Power BI
• Amazon Web Services
• Elasticsearch
• PostgreSQL
• Databricks
• Multivariate statistics

Technical and commercial risk assessment for an investment project in the hardware sector (scaling potential, liability risks, operational processes) Process consulting for two SMEs in the engineering environment, with a focus on workflow digitization and PLM

• Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility

• Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture

• Pipeline orchestration with Azure Data Factory

• Improved and automated the development and deployment process by integrating Git and GitHub Actions

• Technical leadership of the Data Engineering team (3 employees)

• Led workshops to optimize and stabilize the data platform and development process

• Collected and prioritized new requests and maintained the product backlog

• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural

• Fundamentals of electronics measurement, electropneumatic circuits & protective measures according to DIN-VDE
• Installation, wiring and commissioning of PLC-controlled Systems

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• 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 compliance with relevant automotive standards (ECE-R10, CISPR25, ISO-26262). Coordinated cross-country 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) rework, 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. Skilled use of 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 measurements. Developed automated test sequences, programmable endurance tests, troubleshooting routines, data logging, and analysis tools to improve test efficiency and traceability.

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

A nationwide service provider for occupational medicine, occupational safety and health management, focusing on prevention, health and digital transformation.

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

Key areas:

• Establishment and further development of IT project portfolio management
• Analysis and structuring of business and technical requirements
• Management and coordination of cross-functional IT initiatives
• Stakeholder management at department, management and service provider level
• Introduction and further development of project management methods and tools (including Jira, Confluence, MS Planner)
• Preparation of decision-making documents, target visions and implementation concepts in the context of digital transformation
• Management and coordination of transformation, digitalization and organizational projects
• Preparation of decision templates and management documents for executive committees
• Conducting workshops and stakeholder management with departments and IT teams
• Definition of requirements for dashboards, reporting and KPI management
• Participation in the Wissenskompass project (requirements analysis and MVP concept)
• Analysis and evaluation of AI support opportunities 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 departments to further develop project management standards
• Introduction, configuration and training for Microsoft Planner to support project and task management
• Conducting user training and supporting employees in the introduction of new ways of working and tools

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, confirmation and conformity tests for safety-critical braking control units in the railway 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 conformity with 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 and 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 adjustment 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): Work 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 levels (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

Blueprint Works provides Executive Interim Management for technology companies and industrial organisations during critical phases of growth, transformation and industrialisation.

Areas of responsibility:

• Product & Technology
• Program & Project Leadership
• Organisation & Scale
• Industrialisation & Operations

Focus sectors:

• Advanced Manufacturing
• Battery & Energy Technology
• Electrification
• Robotics & Automation
• Industrial Technology
• Deep Tech

• 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.

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

I work as managing director – organization and active development engineer

Recently completed or nearing completion (2023, 2024):

• Projects in the field of wafer production at Hittec Prontor GmbH (apertures for ZEISS SMT GmbH)
• Micromanipulator concepts with flexure joints for ZEISS SMT, arranged through HAYS AG)
• EVE Innovativ GmbH (combined heat and power plants)
• Kraftblock GmbH, (renewable energy storage)
• CAE Consulting Eng. GmbH, European patent for hybrid robot, market development
• TQ Systems GmbH (gearbox technology)

CE is a supplier of development services in the broad field of mechanical engineering. 30 completed projects in the D-A-CH region. Development and patent exploitation company for R&D, products, cutting and welding robots, conveyor technology ... etc.

My most important results:

• Small selection - results of research and development activities led to the international patent application for cutting and welding robots and to the German patent application for the rotary lifting hanger as well as the spherical joint for combining 3 rods at one point for the parallel kinematics robot, push chain, own European patent for hybrid robot, combustion engines

• 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

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