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We have 41 Data Analysis (e learning) PhD Projects, Programmes & Scholarships

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Data Analysis (e learning) PhD Projects, Programmes & Scholarships

We have 41 Data Analysis (e learning) PhD Projects, Programmes & Scholarships

Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning

This project invites PhD candidates who are interested in developing generative models that can deal with complex multimodal patient data, contributing to safer, better and faster innovations of medical products via in-silico trials. Read more

Scalable continuous Machine Learning for non-Stationary Systems

Nonlinear time series prediction attracts researchers from many different disciplines such as engineering systems condition monitoring and control, financial markets prediction and energy management, etc. Read more

Machine Learning and Molecular Modelling in Mass Spectrometry

This PhD project will harness the power of computational modelling and machine learning (A.I.) to analyse data obtained by mass spectrometry experiments and predict structural characteristics of biomolecules and their interactions. Read more

Computer Vision with Deep Learning for Human Data Modelling

This project is focused on learning algorithms to model human data such as human images/video, 3D skeletal motion, 3D body/facial surfaces for computer vision tasks such as recognition, prediction and reconstruction. Read more

Analysing Big Data to Understand Learning

I have access to large existing data sets which contain the potential to show skill development on real-world tasks for large numbers of people (i.e. Read more

Clinical Prediction Modelling under Federated Learning

Clinical prediction models (CPMs) take a set of characteristics about a patient to estimate their risk of an event of interest. Developing CPMs using data that captures observations across multiple clusters (e.g., countries) can increase the robustness and generalisability of CPMs. 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

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