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We have 133 Applied Mathematics (data) PhD Projects, Programmes & Scholarships

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Applied Mathematics (data) PhD Projects, Programmes & Scholarships

We have 133 Applied Mathematics (data) PhD Projects, Programmes & Scholarships

Optimal prediction of dynamical systems with incomplete data

Dynamical systems describe a variety of real-life problems related to evolution over time. It is often the case that the problems are so complicated that mathematical models for them either do not exist or are not accurate enough. Read more

Data-driven optimal prediction of bacteria growth

This project is devoted to an AI-based prediction of bacteria growth and its control by antibiotics. In synthetic biology, an improved understanding of bacterial regulatory circuits is required to develop complex biological systems with functionalities beyond existing in nature [1, 2]. Read more

Data-driven optimal prediction of bacteria growth

This project is devoted to an AI-based prediction of bacteria growth and its control by antibiotics. In synthetic biology, an improved understanding of bacterial regulatory circuits is required to develop complex biological systems with functionalities beyond existing in nature [1, 2]. Read more

High-fidelity CFD and data-driven modelling of aerodynamic noise sources

Applications are invited for one funded 3.5-year PhD studentship for the project titled “High-fidelity CFD and data-driven modeling of aerodynamic noise sources” in the group of Dr Zhong-Nan Wang at the University of Birmingham. Read more

Integrating data-driven methodologies and model reduction for the control of complex networks

This PhD project will combine data assimilation and model reduction methodologies to predict and control functional failures on complex networks, such as catastrophic blackouts in power grids. Read more

Data-driven modeling for crowd dynamics

Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. Data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. Read more

Data driven approaches for nonlinear inverse problems

The project aims to develop new techniques for solving complex inverse problems that arise in various scientific fields. In many real-world applications, such as medical imaging, geophysics, and material science, we often seek to recover the hidden properties of a system from indirect and noisy measurements. Read more

Stochastic modelling and inference for live-cell gene expression time-series data to unravel the mechanisms of stem cell differentiation

  Research Group: Division of Statistics
This project will develop statistical methodology for noisy time-series data and stochastic computational models to analyse live-cell imaging data provided by the lab of our collaborator Dr Cerys Manning at the University of Manchester. Read more

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