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source

Prof YH He

Top university

1 year ago

Multimodal Machine Learning for Healthcare King’s College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully Funded

Deadline

Expired

Country flag

Country

United Kingdom

University

King’s College London

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Where to contact

Official Email

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Keywords

Computer Science
Data Science
Machine Learning
Biomedical Engineering
Electrical Engineering
Medicine
Mathematics
Dentistry
Artificial Intelligence
Natural Language Processing
Computer Vision
Bayesian Statistics
3d Imaging
Technical Engineering
Statistic
Large Language Models

About this position

Multimodal Machine Learning for Healthcare

One fully funded UK/international PhD studentship available in the broad area of statistical machine learning, with a desirable focus on multimodal large language models, uncertainty-aware AI, and/or 3D images. Exemplary applications include Vision Language models for fusion of medical images, videos, and tabular data for disease detection, prediction, and decision making.

Research training

Statistical machine learning foundations and applications, research dissemination skills, interdisciplinary research and communication.

Person specification

Applicants are expected to hold a Bachelor’s degree or above in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university). Past research experience on machine learning, computer vision, natural language processing, or Bayesian inference is preferred.

Additional information

The project is based in the Statistical Machine Learning Group at the Centre for Oral, Clinical, and Translational Studies in the Faculty of Dentistry, Oral & Craniofacial Sciences at King’s College London, with the wider access to resources across King’s including those in the Department of Informatics in the Faculty of Natural, Mathematical & Engineering Sciences.

Funding details

Fully Funded

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