7.4 KiB
Experience: Research Software Engineer — Fraunhofer-Center für Maritime Logistik und Dienstleistungen CML
September 2018 – October 2019 | Hamburg, Germany
Cross-Position Section
Career arc framing: Fraunhofer CML bridges the test-automation / consulting era (Generali, Vizrt) with the data engineering / ML focus that defines Bosch and Swisscom. Here Dennis transitioned from pure software development into applied ML research — working on NLP for sea rescue transcription (ARTUS) and microservice architectures for maritime data exchange (MISSION). He also introduced CI/CD to the team independently. Fixed-term research contract, left by mutual agreement.
CL framing: "At Fraunhofer CML's maritime research center, I moved beyond product software into applied research — developing ML and NLP components for an automatic transcription system for sea rescue (ARTUS), and building the microservice backbone for a maritime data exchange platform (MISSION). I also brought CI/CD discipline to the team by independently setting up Jenkins-based build automation with quality gates."
Achievement FC-1: SCEDAS Crew Scheduling System Development & CI/CD
Source: thiessen_cv_master_profile.md, thiessen_zeugnis_fraunhofer.md User's role: Developer (C# app dev + CI/CD sole setup) Status: Deployed / operational software
Context: SCEDAS® is a Decision Support System for maritime crew scheduling with mathematical heuristics for optimal planning. Dennis developed features, fixed bugs, and independently set up the Jenkins CI/CD pipeline with quality gates — the first build automation at the team.
Bullet variants:
- 2L: Developed and maintained SCEDAS crew scheduling software (C#, .NET, MS SQL, Entity Framework); independently established Jenkins CI/CD pipeline with quality gates, introducing build automation and continuous deployment to the team.
- 3L: Extended and maintained SCEDAS®, Fraunhofer CML's crew scheduling Decision Support System (C#, .NET, MS SQL Server, Entity Framework), improving runtime performance and correctness through increased test coverage; independently introduced Jenkins-based CI/CD pipeline with build automation and quality gates — the first deployment automation adopted by the team.
- 1L: Developed SCEDAS (C#/.NET/SQL) crew scheduling DSS; independently set up Jenkins CI/CD pipeline with quality gates.
Key skills: C#, .NET, Entity Framework, MS SQL Server, Jenkins, CI/CD, build automation, quality gates, software testing ATS keywords: C#, .NET, SQL Server, Jenkins, CI/CD, build automation, continuous deployment Reframing notes:
- Staff/Senior DE: frame CI/CD introduction as initiative signal — independent, not assigned
- ML/AI: LOW — omit SCEDAS; mention only if CI/CD context is relevant
- All roles: CI/CD setup is the key signal here; SCEDAS itself is context
Achievement FC-2: ARTUS — ML/NLP for Sea Rescue Transcription
Source: thiessen_zeugnis_fraunhofer.md, thiessen_cv_master_profile.md User's role: Contributing developer (research team effort — hedge verbs) Status: Research project
Context: ARTUS was a Fraunhofer CML research project to develop an automatic transcription system for sea rescue operations using speech recognition and ML. Dennis contributed ML and NLP development components within the research team.
Bullet variants:
- 2L: Contributed ML and NLP components to ARTUS, a Fraunhofer research project developing an automatic speech transcription system for sea rescue operations — applying speech recognition and machine learning to a safety-critical maritime domain.
- 3L: Developed ML and speech recognition components for ARTUS, a Fraunhofer CML research initiative targeting automatic transcription of sea rescue communications; contributed to the NLP pipeline and model development, bringing machine learning capabilities to a safety-critical domain with no prior automated transcription tooling at the center.
- 1L: Contributed ML/NLP components to ARTUS — sea rescue speech transcription system (Fraunhofer research).
Key skills: ML, NLP, speech recognition, Python, research, maritime safety ATS keywords: NLP, machine learning, speech recognition, Python, research Reframing notes:
- ML/AI: HIGH — lead as evidence of applied ML/NLP research experience
- Staff/Senior DE: MED — include as ML breadth signal; hedge verb "Contributed"
- Analytics/Platform: LOW — omit
Provenance: "Entwicklungstätigkeiten" in Zeugnis = development work within a research team. Use "Contributed" not "Led". Do not claim sole ownership of ARTUS.
Achievement FC-3: MISSION — Maritime Microservice Data Exchange Platform
Source: thiessen_zeugnis_fraunhofer.md, thiessen_cv_master_profile.md User's role: Developer (microservices for the platform) Status: Research prototype
Context: MISSION was a Fraunhofer CML research project to build a maritime data exchange platform. Dennis built microservices using Express.js, JavaScript, Docker, and SQLite to enable data exchange between maritime stakeholders.
Bullet variants:
- 2L: Built microservices (Express.js, JavaScript, Docker, SQLite) for MISSION, a Fraunhofer research project creating a maritime data exchange platform — enabling structured data interchange between maritime logistics stakeholders.
- 3L: Developed the microservice layer for MISSION, a Fraunhofer CML research platform for maritime data exchange — built REST services in Express.js and JavaScript, containerized with Docker, and backed by SQLite; enabling data sharing across maritime logistics actors including ports, operators and research partners.
- 1L: Built Express.js/Docker microservices for MISSION — Fraunhofer maritime data exchange research platform.
Key skills: Express.js, JavaScript, Docker, SQLite, microservices, REST APIs, research prototyping ATS keywords: microservices, Docker, REST, Express.js, JavaScript, containerization Reframing notes:
- Staff/Senior DE: frame as microservice/Docker evidence from pre-Bosch period
- ML/AI: LOW — omit
- Data Platform/Infra: include for early Docker/microservice signal
Achievement FC-4: Predictive Maintenance Research Grant Contribution
Source: thiessen_zeugnis_fraunhofer.md User's role: Contributor ("Mitarbeit" = participated) Status: Grant proposal (not an outcome claim)
Context: Contributed to a Fraunhofer research grant proposal targeting ML-based prediction of optimal maintenance timing for maritime equipment.
Bullet variants:
- 1L: Contributed to Fraunhofer research grant proposal for ML-based predictive maintenance in maritime operations.
Key skills: ML, predictive maintenance, research grant writing, maritime domain ATS keywords: predictive maintenance, machine learning, research Reframing notes:
- ML/AI: include as minor signal if space allows
- All others: LOW — omit; rarely warrants a dedicated bullet on resume
Position Summary
| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
|---|---|---|---|---|---|
| SCEDAS + CI/CD | FC-1 | HIGH | LOW | LOW | MED |
| ARTUS ML/NLP | FC-2 | MED | LOW | HIGH | LOW |
| MISSION Microservices | FC-3 | MED | LOW | MED | MED |
| Predictive Maintenance Grant | FC-4 | LOW | LOW | MED | LOW |