# 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."

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### 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

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### 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.

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### 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

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### 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

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## 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 |
