R2T2-Med：用于可扩展且可部署医学图像分析的资源感知AI博士研究员
PhD Studentship: R2T2-Med: Resource-Aware AI for Scalable and Deployable Medical Image Analysis
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
AI 中文速览
- 研究内容
- 本项目旨在开发一个用于2D和3D医学成像的资源感知AI框架，以适应不同案例的复杂度、临床风险及可用硬件。研究将探讨如何减少医学影像模型中的不必要计算，开发可针对不同资源预算进行配置的灵活模型系列，并在全人群腹膜MRI分析和床旁皮肤病学成像中进行验证。
- 申请条件
- 原文未说明具体申请条件。
- 待遇
- 提供2026/7年度的学费资助以及每年21,805英镑的生活津贴。
- 申请方式
- 原文未说明具体的申请方式、截止日期或提交地址。
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 截止
- （Europe/London） 剩 40 天
- 学科
- 计算机科学
- 合同类型
- 雇佣合同
- 原文薪资
- GBP 21,805 / 年（税前）
- 税后月薪（估）
- ¥13,700；房租后 ¥5,400
- 估算假设
- 单身、无子女、雇佣合同的粗略估算，以学校 offer 为准；扣除率 18%；汇率日期 2026-10-01
- 原帖发布
- 本站收录
- 内容更新
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-28
- 详情核验
判定依据（原文摘录）
- 这是一个博士培养项目
PhD Studentship: R2T2-Med: Resource-Aware AI for Scalable and Deployable Medical Image Analysis
- 奖学金提供学费和每年21805英镑的生活津贴
The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.
原文
Medical imaging generates very large and rapidly growing volumes of data. Although AI can support automated image analysis, many current models require substantial computing power, memory and processing time. This limits population-scale research, increases deployment costs and restricts adoption in healthcare settings with limited hardware or connectivity. There is therefore a need for medical AI that is not only accurate, but also efficient, reliable and practical to deploy. Research aim and details: This project aims to develop a resource-aware AI framework for both 2D and 3D medical imaging. The framework will adapt its computational requirements to the complexity and uncertainty of each case, the clinical risk and the available hardware.
The project will investigate where unnecessary computation occurs within medical imaging models and how it can be reduced without losing clinically important information. It will then develop a flexible model family that can be trained once and configured for different resource budgets. Safety will be embedded Engineering and Physical Sciences Research Council Doctoral Landscape Award throughout the research by assessing whether efficiency measures affect particular anatomies, clinical sites or patient groups disproportionately. It will be validated in two complementary settings: population-scale abdominal MRI analysis using UK Biobank and near-patient dermatology imaging using public and established clinical data pathways.
The research will also examine whether improved efficiency can be achieved without reducing performance or disproportionately affecting particular clinical sites or patient groups. Expected outcomes: The project will deliver an adaptive and deployable medical imaging AI framework, reproducible efficiency benchmarks, reusable software and evidence from both large-scale and resource-constrained settings. It targets substantial reductions in processing time and memory while maintaining accuracy, reliability and subgroup safety. The resulting technology will reduce computational and hardware requirements, enabling more scalable, sustainable and equitable use of medical imaging AI across research, NHS and commercial healthcare environments.
Funded by: EPSRC Engineering and Physical Sciences Research Council £21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.