About Me
Hello, I’m Thomas Rauter, a PhD researcher in Bioinformatics at the University of Salzburg. My PhD focuses on statistical evaluation of time-series omics data, CHO cell modeling, and interpretable deep learning for molecular networks.
Background
I hold a bachelor’s degree in Molecular Biology from Graz and a master’s degree in Biotechnology, where I developed a strong interest in computational methods, particularly machine learning. This passion led me to specialize in bioinformatics for my PhD research.
Current Work
As part of my PhD, I aim to:
- Develop statistical methods for analyzing time-series omics data.
- Build interpretable machine learning models for molecular networks.
- Model CHO cells to improve biotechnological processes.
My Goal: Becoming a Full-Stack Data Scientist
My ultimate goal is to become a full-stack data scientist, a role that encompasses not only developing machine learning models but also deploying them into production and monitoring them, to deliver real-world value.
A full-stack data scientist bridges the gap between model development and its practical application. While I love programming, machine learning, and data science, I believe that the true value comes from delivering functional solutions that directly impact the business.
A model in a Jupyter notebook, no matter how sophisticated, cannot provide value to a company on its own. Deployed models, integrated into systems and accessible to end-users, are what drive decision-making and innovation. This is why I am passionate about mastering the entire lifecycle of data science projects, from exploration and modeling to deployment and maintenance.
Strenghts
- Autodidactic Learning: I have a proven ability to teach myself complex
technical topics through independent study. With a formal background in
biology, I transitioned into data science without relying on additional
university coursework. By leveraging online resources, technical
documentation, and hands-on experimentation, I developed expertise in
statistical modeling, data analysis, and machine learning using Python and R. This self-directed approach allows me to efficiently master new tools and concepts in fast-evolving domains. - Structured Thinking: I’m highly organized by nature—whether it’s my desk, desktop, phone, browser tabs, emails, or codebase, everything is intuitively named, neatly arranged, and well-documented. I use to-do lists extensively and rarely lose track of data or files. This structure isn’t just a habit—it’s a conscious strategy. Long-term projects live or die by their organization, and I make sure mine stay alive.
- Patience: Structured work over time requires patience, and I’ve always had a strong sense of that. Progress in research or data science is often slow and incremental, so staying focused on the bigger picture is essential. Since I started studying in 2017, I’ve wanted to apply my skills to projects that have real-world impact. But I knew I first needed to build them—and only in 2024, with the start of my PhD, did I feel ready to do that meaningfully.
- Creativity: I tend to think in unconventional ways, which often leads to efficient or elegant solutions. For example, back in school, I’d sometimes forget the “proper” formulas for math problems but still solve them using graphical reasoning or approximation. That mindset stuck—I look for insight, not just instructions.
Where I am improving
- Prioritization: I sometimes gravitate toward tasks I find interesting, even when they aren’t the most urgent. This can delay higher-priority work. I’ve become more aware of this tendency and am now building routines that help me stay aligned with what matters most to the project—without losing the drive for exploration.
- Focus: I can get distracted by noise and interruptions, especially in shared office environments. To counter this, I use earplugs, limit notifications, and set clear focus blocks to stay fully immersed in one task at a time.
Explore More
For more details, you can check out: