Terezia Slaninakova

Terezia Slaninakova

Ph.D. candidate at Masaryk University

About me

Hi! I am Terezia Slaninakova, Ph.D. candidate (finishing in 2026) and researcher at Masaryk University, Brno, Czech Republic.

I am a research engineer focused on building useful production-ready applications for processing of complex data.

I got my B.S.c-equivalent in Computer Science in Brno University of Technology in 2018, in my final thesis I focused on generative AI applied to sound in Speech@FIT lab. While studying I also worked at Y Soft and Adacta on creating automatic quality assurance pipelines.

For my M.S.c, I studied Artificial intelligence and Data processing, while working on projects at CERIT-SC, where I created ML pipelines for life tissue and remote sensing data. For my thesis, I created an ML-based index high-dimensional vector data.

I began my Ph.D. in Spring 2021, focused on extending my work on the ML-based vector index under the GF23-07040K project co-lead with CAU in Kiel, Germany. The theoretical work ultimately lead to creating a similarity search system for finding proteins with similar structure in 200M+ proteins, AlphaFind, used by researchers worldwide. Recently, we extended its functionality with AlphaFind v2, indexing a total of 1B data. While on Ph.D., I attended three internships - in Daisy lab under prof. Jensen in Aalborg, Denmark, in Database Sysems and Data Mining group under prof. Kröger in CAU, Kiel, Germany, and, most recently, in Greener group at MRC-LMB in Cambridge, UK.

I am currently working on a large-scale similarity search system for molecular dynamics simulations and using vector search + RAG systems for improving search for czech research data within the eosc-cz project. I’m finishing my Ph.D. in 2026 and exploring opportunities where I can apply my expertise in similarity search, ML systems, and production deployment. If you’re working on interesting problems in vector search, RAG systems, scientific ML, or related areas, I’d love to connect!

Core Expertise

  • Similarity Search & Retrieval: Vector databases, ML-based indexing, hybrid search architectures, semantic search systems

  • ML Systems: PyTorch, embedding models, data processing pipelines, evaluation frameworks

  • Infrastructure: Kubernetes, Docker, batch processing, production deployment

  • Domains: Protein structure analysis, molecular dynamics, high-dimensional data

If you would like to reach out or learn more, please, write me an email :).