UVC Partners: Data & AI - Master Thesis / Internship (f/m/x)

UnternehmerTUM gGmbH · München Kreativquartierjunior

In short

▸Trabajo de tesis o prácticas en inteligencia de inversión con IA, enfocado en aplicaciones reales de ML y datos.
▸Día a día: investigar, prototipar y presentar soluciones con GNNs, modelos programados o actualización bayesiana, con autonomía y mentoría.
▸Lo destacable: trabajar directamente con inversores y equipos técnicos en un entorno de desarrollo impulsado por IA, con impacto real en decisiones de inversión

Fluent in English, including technical language. It is what you will work, write, and pres

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What they ask for

✓Estudiante de máster en Ciencias de la Computación, Ciencia de Datos o áreas similares.
✓Profundidad o ambición en al menos una herramienta: GNNs, LLMs, evaluación de herramientas, modelos probabilísticos.
✓Python limpio y modular, fundamentos sólidos en ML y estadística.
✓Experiencia en proyectos autónomos con dirección propia y resultados visibles.
✓Fluidez en inglés técnico (escrito, hablado, presentaciones).
✓Capacidad crítica con herramientas de codificación asistida por IA (no solo usarlas, sino revisarlas).

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PyTorch GeometricLLM toolingTool use and evaluationPyMCNumPyroGraph MLGraph databasesProgram-aided Language ModelsBayesian UpdatingAI-assisted coding tools

Who should you write to at UnternehmerTUM gGmbH?

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About us UVC Partners is one of Europe’s leading early-stage venture capital firms. With our fourth fund generation of €250 million, we invest in disruptive B2B startups at the seed and Series A stage across Europe. Our focus areas include enterprise software, mobility, and industrial technologies. auto, Proxima Fusion, and Aleph Alpha. At UVC Partners, we’re driven by a deep passion for entrepreneurship and its power to positively shape society. Environmental and social responsibility are core to our DNA. We hold ourselves to the highest standards in everything we do. As a firm, we continuously reinvent ourselves to stay ahead and best serve our portfolio and investors. We believe in ownership over hierarchy and foster a culture built on collaboration, curiosity, and personal engagement. And while we’re serious about our mission, we also genuinely enjoy working together and building lasting relationships along the way. Your mission You will join a specialized team working at the intersection of applied research and venture capital, side by side with investors, AI researchers, and engineers building the backbone of our investment intelligence. This is a hands-on role with real stakes. You will be treated as a full member of the team, expected to take a research question from literature to a working prototype and to present your results to the people who make the investment decisions. Your profile You are enrolled in a Master’s program in Computer Science, Data Science, Data Engineering, Information Systems, or a similar program at a top university. g. g. PyMC, NumPyro). You write clean, modular Python and have firm machine learning and statistics fundamentals. You are fluent with AI-assisted coding tools and you have judgment about them. You know what they do well, where they quietly fail, and you review everything they produce. You do not ship AI slop. You work independently. You have run a project where you set the direction yourself, and you can point to it. You are fluent in English, including technical language. It is what you will work, write, and present in. German is helpful but not required. How we work We are an AI-powered development team . Everyone here codes with AI assistance, and we treat that as a craft. You need to know which parts of a problem to hand over, where these tools fail quietly, and how to review output you did not write. Agency matters a lot in our team. You get mentorship, context, and access to everyone. You do not get someone assigning you tasks every morning. Take a rough problem statement, come back with an approach, show drafts while they can still change, and flag what is not working. What you bring should outweigh what it costs us to support you. Potential Research Directions These are directions we could explore, with scope defined together at the start based on your background and where the work leads. 1. With Graph Neural Networks (GNNs) we would work on a graph-structured database, where people and companies are nodes and the relationships between them are part of the data, and ask what that structure can tell us that a table cannot. 2. With Program-aided Language Models (PAL) we would have the model write a small program for every number it pulls from decks and financial statements, so figures are accurately computed, because that is what makes it safe to point a language model at financial data. 3.

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