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Master Thesis: Multimodal Knowledge and Reasoning for Biomedical Safety Applications
Cognitive Systems —München
Graph machine learning: graph neural networks (GNN / GAT / RGCN) or knowledge-graph embeddings. Multimodal learning: experience fusing heterogeneous input types…
4 Tage
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Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations.

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Master Thesis: Multimodal Knowledge and Reasoning for Biomedical Safety Applications

Cognitive Systems
München

Jetzt bewerben

Details zur Stelle

vor 4 Tagen

Qualifikationen

Master
Machine Learning
Neo4j
Graphdatenbanken
Python

Vollständige Stellenbeschreibung

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world's leading organizations for application-oriented research. 74 institutes develop pioneering technologies for our economy and society – more precisely: 30 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow.

Safe Intelligence — this forms the core brand of the Fraunhofer Institute for Cognitive Systems IKS. Connected cognitive systems drive innovation in many sectors, including mobility, healthcare, and automation in industry. Disruptive technologies such as artificial intelligence and quantum computing play a key role here. Fraunhofer IKS is conducting research to ensure that these applications are reliable and verifiably safe. We consider resilience and intelligence to be part of the same process.

Be part of change

In this master thesis, you will work at the intersection of multimodal machine learning, knowledge representation, and Reasoning in biomedical safety - contributing to a research system that must reason reliably over heterogeneous data sources.
Your work will span three interconnected challenges:

Multimodal data integration: combining structured data (graphs, ontologies, relational databases), unstructured text (scientific literature, clinical reports), and molecular or numerical features into a unified reasoning framework.
Knowledge-grounded inference: designing retrieval and reasoning pipelines that ground model output in interpretable, traceable knowledge paths.
Uncertainty quantification and faithfulness: developing methods to certify when model predictions are well-supported by evidence and communicating confidence in a way that supports human oversight.

You will engage with the full research lifecycle, literature review, problem formalization, system design, implementation, and empirical evaluation against published baselines. You will have the opportunity to contribute to an applied research project with real-world deployment context.

What you contribute

Essential

Strong Python programming skills and good software engineering practices (modular design, version control, documentation)
Solid foundation is one of the following:

1. Multimodal learning: experience fusing heterogeneous input types such as text, graphs, structured tables, or molecular representations
2. Knowledge graphs: construction, graph data models, traversal, or graph databases (Neo4j / Cypher ideally)
3. Retrieval-augmented generation (RAG) and LLM integration (LangChain, LlamaIndex, or equivalent)

Ability to independently read, understand, and synthesize primary research literature
Structured, self-driven working style with attention to reproducibility

Advantageous

Graph machine learning: graph neural networks (GNN / GAT / RGCN) or knowledge-graph embeddings
Generative AI knowledge: agentic workflows and multi-agent systems
Biomedical or life science domain knowledge (ontologies, clinical data formats, omics pipelines)
Working with messy real-world data: missing values, label noise, domain shift

Profile

Enrolled at a German university; The candidate should be able to come to the office in Garching at least one day a week.
Especially suitable for M.Sc. Computer Science, Bioinformatics, Data Engineering, Computational Life Sciences, or related disciplines
Genuine curiosity about trustworthy AI and its application in high-stakes domains

What we offer

Approachable supervisors and integration into a dynamic interdisciplinary team spanning AI safety, data science, and application domains.
Hybrid set up with workplace at our modern institute building in Garching-Forschungszentrum, close to TU Munich.
Hands-on experience with real-world data pipelines, HPC infrastructure, and Fraunhofer’s applied research practices.
Work on open scientific questions within an active applied research project, with a clear path toward publication at international venues.

We value and promote the diversity of our employees' skills and therefore welcome all applications – regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Ready for a change? Then apply now and make a difference! Once we have received your online application (Motivation letter, CV and a current transcript of records), you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

Technical questions will be answered gladly by
Anitha Bhat Talagini Ashoka, eMail: [email protected] (please apply only through the application link provided in the job posting)

Fraunhofer Institute for Cognitive Systems IKS

Requisition Number: 85051 Application Deadline:

Jetzt bewerben

Erweiterte Suche

applied ai, senior/staff forward deployed machine learning engineer Jobs in münchen, by

Master Thesis: Multimodal Knowledge and Reasoning for Biomedical Safety Applications

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