Within ScaDS.AI Dresden/Leipzig, we welcome new researchers to our teams in Dresden and Leipzig. This page lists suggestions for open topics for Research Associates / PhD Students (f/m/x) that are currently available within the Graduate School, together with mentors and host institutions. Feel free to look around to decide which topics excite you most and best match your skills!

We are looking forward to your application! Please submit your detailed application with the mentioned documents quoting the job number w25-063 by August 28, 2025. Please add the job-ID (e.g. T2 or T5.4) to your subject, when submitting your application. Without the exact job-ID your application can NOT be considered.

Open Topics

Knowledge representation and inference

T2 Combining Description Logics with Argumentation Frameworks for Repair

Belongs to project: Combining Description Logics with Argumentation Frameworks

Supervisors: Prof. Dr.-Ing. Franz Baader, Prof. Dr. Ringo Baumann

Location: Dresden

% of Position: 100%

Abstract: Description logics and argumentation frameworks are prominent symbolic AI formalisms, offering complementary approaches to knowledge representation. Description logics provide ways to define key notions of an application domain and support reasoning to derive new knowledge. Argumentation frameworks represent arguments and their relationships (such as attack and support) and facilitate conflict resolution through various argumentation semantics. Despite their prominence in knowledge representation, there has been little interaction between these two areas so far. The project investigates their combination, with the goal of constructing methods for improving the quality of argumentation frameworks and description logics knowledge bases used in applications, and thus improving the trust in the results produced by the investigated symbolic AI methods.

Mathematical foundations of AI and representation learning

T5.4 Scientific Application: Latent Geometry of Information Processing in Complex Dynamical Systems

Belongs to project: Systems AI: A High-Dimensional Dynamical Systems Perspective on Neural Networks

Supervisors: Prof. Dr. Frank Cichos, Dr. Nico Scherf

Location: Leipzig

% of Position: 75%

Abstract: Join our PhD project exploring emergent complexity and latent geometry in real microscopic physical systems, bridging advanced computational tools and experimental setups. We investigate active particle ensembles and random photonic media to discover how high-dimensional dynamics can be captured and controlled in low-dimensional latent spaces. By correlating neural network states with real-world many-body systems, we reveal how large-scale complexity emerges from simple building blocks. This approach enables novel feedback loops and coarse-graining perspectives, driving insights into the interplay of physics and AI, and opening paths to robust, low-energy computational substrates.