Sort by
Refine Your Search
-
models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
-
federated knowledge graph framework that facilitates the querying, consolidation, analysis, and interpretation of distributed proteomics-focused clinical knowledge graphs. To achieve this, we will employ
-
), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued. Mathematical skills: Competence in mathematical modeling of dynamic systems and
-
sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued
-
knowledge-graph groundedfactuality in LLM. The PhD students will work both independently and collaboratively within the group, and will have opportunities to engage with national and international partners
-
XAI methods, e.g. counterfactuals in reasoning and knowledge graphs (KGs) based on domain expertise, to strengthen inferences drawn from data, and to reduce complexity of learning – by factual reasoning