About this role
Summary: This master's thesis investigates the relationship between mechanistic interpretability methods and activation vector spaces in neural networks. The research focuses on whether targeted matrix transformations can strengthen or weaken safety features in large language models, requiring strong Python skills and knowle...
Background and Motivation
Activation functions are a fundamental part of machine learning, as they introduce a non-linear component and thus make it possible to approximate arbitrary functions. In practice, these functions can have several million dimensions per layer of a neural network, which lead to the model’s prediction layer by layer through linear combinations and subsequent activation. Algebraically and numerically, it is also possible to transform the matrices in such a way that the products remain the same, while the factors are chosen cleverly so that individual features can be emphasized more strongly.
Goal
The thesis will investigate how exactly methods of mechanistic interpretability relate to the vector spaces of the activations, and whether, for example, safety features can be strengthened or weakened through targeted transformation of the matrices.
Results
The thesis should show whether manual transformations of the parameters are possible in principle, and whether a careful choice of bases, for example through orthogonalization, yields linear combinations that allow individual capabilities to be influenced in a targeted way.
Be part of change
- Research and implement novel machine learning approaches that improve the safety of LLMs
- Critically evaluate your results
- Present your results
- Write a project report in the form of a master’s thesis
What you contribute
- Very good academic performance with an average grade of 2.2 or better (German grading scale)
- Knowledge of machine learning, including training, inference and optimization of transformer architectures
- Knowledge of algebra and numerical analysis is helpful
- Knowledge of AI safety is desirable
- Good Python skills are essential and will be tested during the interview
- Interest in science and current research projects
What we offer
- Flexible working hours
- Insights into the intersection of academic research and industrial application
Related Work
[1] https://arxiv.org/pdf/2404.09932
[2] https://openreview.net/pdf/1e69f75870a7fc9de5d820991191c2377690b0aa.pdf
[3] https://arxiv.org/pdf/2511.20273
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, 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.
Fraunhofer Institute for Secure Information Technology SIT
<a href="http://www.sit.fraunhofer.de<input id="tor\_\_fcustInstituteLinkRMK" type='hidden' value='27335' >" target="\_blank" rel="noopener">www.sit.fraunhofer.de
Requisition Number: 86082 Application Deadline:
[Not translated in selected language]