帝国理工学院PhD position in Deep Learning Models for Dissolution Dynamics申请条件要求-申请方
主办方
帝国理工学院
PhD直招介绍
About the Project
Project description:
Current tools for predicting oral drug performance, such as the TIM-1 system and computational fluid dynamics (CFD) models, provide valuable insight under controlled in vitro conditions but cannot capture variability in gastric geometry and motility across patients. As a result, predictions often fail to reflect in vivo performance, particularly for complex formulations such as amorphous solid dispersions. This project will develop geometry-conditioned neural operators that learn from CFD simulations of drug dissolution and precipitation in patient-specific gastrointestinal geometries. By combining physics-based simulation with deep learning, the framework will enable accurate, patient-level prediction of oral drug performance.
This PhD studentship is a part of CEDAR. CEDAR is an 8.5-year programme funded by an EPSRC( https://www.ukri.org/councils/epsrc/ ) Centre for Doctoral Training (CDT) in cyber-physical systems for medicines manufacturing. Through this project, PhD researchers will have a unique opportunity to collaborate with industry leaders to build a comprehensive toolkit for digital and advanced medicine manufacturing processes."
Research challenges addressed in this project:
This project bridges AI, computational fluid dynamics, and pharmaceutical science to revolutionise how we predict oral drug performance. You will develop geometry-conditioned deep learning models that learn how drug dissolution and precipitation behave inside patient-specific gastrointestinal geometries derived from MRI or CT data. By combining machine learning with physics-based simulations, you’ll create fast models on patient-centric drug dissolution dynamics. This project offers the opportunity to work at the cutting edge of pharma innovation, geometry-aware AI, and personalised medicine.
帝国理工学院 PhD position in Deep Learning Models for Dissolution Dynamics项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Funding and eligibility:
The studentships cover home tuition fees, research/training costs, and provide a monthly stipend for 4 years (minimum annual stipend of £20,780, tax-free). Part-time opportunities may be available, please contact the skills@cmac.ac.uk for more information.
帝国理工学院Phd申请条件和要求都有哪些？PhD position in Deep Learning Models for Dissolution Dynamics项目是不是全奖？有没有奖学金？下面我们一起看一下帝国理工学院申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Entry requirements:
A minimum of a 2:1 Honours degree (or international equivalent) in chemical engineering, chemistry, computer science, data science, electrical engineering, materials science, mechanical engineering, pharmaceutical sciences, physics, or a relevant science or engineering discipline.
An MSc is desirable.
For international students whose first language is not English, an IELTS score of 6.5 (with no less than 5.5 in any element) is required.
报名方式
招生人信息
Dr NB Basha, Prof O Matar
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