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We have 54 Applied Mathematics (machine) PhD Projects, Programmes & Scholarships

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Applied Mathematics (machine) PhD Projects, Programmes & Scholarships

We have 54 Applied Mathematics (machine) PhD Projects, Programmes & Scholarships

Machine Learning and Domain Decomposition methods for Fluid Dynamics

Modelling of many modern applications leads to linear systems whose size is too large to allow the use of direct solvers. Thus, parallel solvers are becoming increasingly important in scientific computing. Read more

Mathematical Machine Learning for Molecular Modeling

Project description. This PhD project aims to develop Machine Learning methods for Molecular Modeling with a particular focus on aspects relevant to dynamics preserving coarse-graining strategies. Read more

Understanding the evolution of gene regulatory networks through biophysical modelling and machine learning

The ability to coordinate the expression of genes within a cell is at the heart of life. When and how much of a specific gene is turned into a protein is essential for organisms to respond to their environment and to manage resources. Read more

Connections between Numerical Analysis of Differential Equations and Machine Learning

Up to two funded PhD projects are available in the Department of Mathematics at the University of Manchester on Connections between Numerical Analysis of Differential Equations and Machine Learning. Read more

Space Trajectory Classification using Machine Learning

Supervisory Team: Dr Alexander Wittig, Dr Davide Amato (external, from Imperial). Project description. Interest in more complex dynamics of space trajectories beyond perturbed Keplerian dynamics is rapidly growing. Read more

Exploring close binary stars: Using nonlinear time series analysis and machine learning for analysing stellar light curves.

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Read more

Characterisation of tissue microstructure from non-invasive MRI using Machine Learning

The characterisation of biological tissue microstructure in vivo and non-invasively is of outmost interest in science. If successful, it could reveal unique insights into biological processes, including aging and cancer. Read more

Machine Learning approaches to improve the efficiency of fluid dynamics simulations

OpenFOAM and CFD simulations are often computational expensive both in terms of resources and time. CFD codes often use explicit methods that require small time steps of the order of micro-nano seconds. Read more

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