Computer Science (markov) PhD Projects, Programmes & Scholarships

We have 7 Computer Science (markov) PhD Projects, Programmes & Scholarships

Advanced Monte Carlo simulation of quantum fields: taking worldline sampling beyond the diffusive regime

University of Warwick School of Engineering

University of Warwick – School of Engineering. Qualification aim. Doctor of Philosophy in Engineering (PhD). Start date: 30 September 2024. Read more

Supervisor: Dr MF Faulkner

31 July 2024 PhD Research Project Funded PhD Project (UK Students Only)

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Integrated High Altitude Platform: LEO Satellite Mega-Constellations for Next Generation Non-Terrestrial Networks

University of York School of Physics, Engineering and Technology

Research Group: Communication Technologies

LEO satellite mega constellations promise to revolutionise wireless connectivity in many areas of the world. However, they have a fundamental drawback in that conventional cellular user equipment cannot be used directly. Read more

Supervisor: Prof D Grace

Year round applications PhD Research Project Self-Funded PhD Students Only

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Artificial Intelligence enabled Distributed Edge-Centric Cellular Networks

University of York School of Physics, Engineering and Technology

Research Group: Communication Technologies

Today, wireless cellular networks rely heavily on the core network to deliver services. This introduces latency and other inefficiencies. Read more

Supervisor: Prof D Grace

Year round applications PhD Research Project Self-Funded PhD Students Only

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Computer modelling the development of organisations in the high-tech entrepreneurship ecosystem

Kingston University Faculty of Engineering, Computing and the Environment

Technological firms are regarded as key for national and regional economic development. Today, more than ever before, the business environment is burgeoning with innovation; Even simply Googling for apps and services to help boost elementary office efficiency, returns over 2000 items. Read more

Supervisor: Dr R Mellor

Year round applications PhD Research Project Self-Funded PhD Students Only

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Machine Learning and AI for Optical System Optimization

Heriot-Watt University School of Engineering & Physical Sciences

Machine Learning and AI for Optical System Optimization. Read more

Supervisors: Dr R Carter, Dr M Koskinopoulou

Year round applications PhD Research Project Funded PhD Project (European/UK Students Only)

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Probabilistic Approaches to Artificial Intelligence

Lancaster University Department of Mathematics and Statistics

We are currently seeking a number of PhD researchers in areas of Probability, Statistics and Machine Learning for the £8.5m EPSRC-funded hub on Probabilistic AI (Prob_AI Hub). Read more

Supervisors: Prof P Fearnhead, Prof Chris Nemeth

Year round applications PhD Research Project Funded PhD Project (UK Students Only)

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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems

University of Sheffield Department of Automatic Control and Systems Engineering

This project will focus on the development of Bayesian inference methods such as sequential Markov Chain Monte Carlo (MCMC) methods for filtering and smoothing of states and parameters of complex nonlinear systems. Read more

Supervisor: Prof L Mihaylova

Year round applications PhD Research Project Self-Funded PhD Students Only

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We have 7 Computer Science (markov) PhD Projects, Programmes & Scholarships

Computer Science (markov) PhD Projects, Programmes & Scholarships

We have 7 Computer Science (markov) PhD Projects, Programmes & Scholarships

Advanced Monte Carlo simulation of quantum fields: taking worldline sampling beyond the diffusive regime

Integrated High Altitude Platform: LEO Satellite Mega-Constellations for Next Generation Non-Terrestrial Networks

Artificial Intelligence enabled Distributed Edge-Centric Cellular Networks

Computer modelling the development of organisations in the high-tech entrepreneurship ecosystem

Machine Learning and AI for Optical System Optimization

Probabilistic Approaches to Artificial Intelligence

Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems

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