Sort by
Refine Your Search
-
Computational Astrochemistry/Algorithm development for Quantum Dynamics Calculations School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof AJHM Meijer Application
-
Computational Circular Design: Development, scalability and computational efficiency of surrogate-assisted many-objective optimisation algorithms for circular design for disassembly (C3.5-AMR
-
Computational Circular Design: Development, scalability and computational efficiency of surrogate-assisted many-objective optimisation algorithms for circular design for disassembly (C3.5-AMR
-
problems. A key aim of this PhD is therefore to develop novel IRL algorithms tailored to this setting, capable of extracting hidden reward functions from real-world, imperfect data. This will allow us to ask
-
extraction? What would be the best choice of solvent? What is the optimal route to recycle the water in the fermentation broth? Answering these questions requires us to develop new design algorithms. It is
-
are inherently highly complex. In this research project you will use state of art AI-based optimization algorithms to develop new functionality into industry-relevant digital design tools (CAD) to support
-
Model Based Design and Flight Testing of a Vertical Take-Off Vertical Landing Rocket (C3.5-MAC-John)
Landscape Award at the University of Sheffield. This project will develop a Vertical Take-Off Vertical-Landing (VTVL) rocket, also known as a “propulsive lander” or “hopper”. The technologies developed and
-
, autonomous learning agents are likely to take an active role in human society, engaging in daily interaction and collaboration with humans. Developing learning algorithms that enable these agents to produce
-
into their training process. By fusing the theoretical rigour of control theory with the flexibility of deep learning, the goal is to develop a new framework to train magnetic control algorithms for tokamak reactors
-
with emerging neuroscience evidence that synapses perform nonlinear transformations rather than merely transmitting signals. This project aims to develop and analyse local learning rules for KANs