Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
11th October 2026 Languages English English English The Department of Structural Engineering has a vacancy for a PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High
-
interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
-
of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control
-
, but for example: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical modeling of hormone rhythms Qualifications and personal qualities: We
-
learning models for segmenting and interpreting forest point clouds and rebuild them as real-time, incremental estimation systems. The work supports navigation, self-localization, and traversability work
-
leader is Associate Professor Soledad Gonzalo Cogno. About the project The successful candidate will contribute to the development of mathematical and computational models to enquire about the mechanisms
-
when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
-
renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system
-
of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
-
to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the