绿色屋顶与立面低碳抗热建筑人工智能优化的博士研究员
PhD Studentship: AI Optimisation of Green Roofs and Facades for Low-Carbon, Heat-Resilient Buildings
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
- 该项目旨在开发一个基于人工智能的框架，用于优化建筑集成的绿色基础设施（如绿色屋顶和绿色立面）。研究内容包括预测绿色屋顶和立面的能源、碳及热效应，识别适宜的配置方案，评估当前及未来气候条件下的表现，并通过多目标优化平衡多方考量。
- 申请条件
- 申请人应持有或预计获得建筑工程、建筑学、土木或环境工程、计算机科学、数据科学、地理学或相关学科的优秀本科学位（一等或二等荣誉上`)以及获得优异或优秀（Merit或Distinction）成绩的硕士学位。申请人需具备定量与分析能力、Python编程经验、人工智能或机器学习经验，并对低碳建筑、气候适应和城市可持续发展感兴趣。海外非英语国家申请者雅思总分须达到6.5分且单项不低于6.0分。
- 待遇
- 提供全额学费以及每年18,000英镑的免税生活补助。项目为期36个月，拟定于2027年9月入学。
- 申请方式
- 申请人必须通过上方的申请按钮提交在线申请表。如需进行非正式咨询，可联系 Ali Bahadori-Jahromi 教授，邮箱为 ali.jahromi@uwl.ac.uk。
- 材料清单
- 使用博士申请模板准备的研究计划大纲
- 当前简历
- 最高学历成绩单
- 来自非英语国家的海外申请者的英语语言能力证明
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 90 天
- 学科
- 能源
- 合同类型
- 雇佣合同
- 原文薪资
- GBP 18,000 / 年（税前）
- 税后月薪（估）
- ¥11,300；房租后 ¥3,000
- 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
- 原帖导师候选（待核验）
- Ali Bahadori-Jahromi
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-26
- 详情核验
判定依据（原文摘录）
- is_phd
Applications are invited for a funded PhD to develop an AI-enabled framework for optimising building-integrated green infrastructure.
- english_ok
Overseas applicants from non-English speaking countries must have an IELTS (International English Language Testing System) score of 6.5 overall, with no individual component scoring below 6.0.
原文
Applications are invited for a funded PhD to develop an AI-enabled framework for optimising building-integrated green infrastructure.
Green roofs and green façades can reduce building energy demand, lower operational carbon emissions and improve local thermal conditions. However, their performance varies according to building type, design, location, vegetation and climate. This project will investigate how artificial intelligence, building-performance modelling and geospatial analysis can be combined to identify effective green-infrastructure solutions for different buildings and urban settings.
Research objectives
The successful candidate will develop and test a decision-support framework to:
• predict the energy, carbon and thermal effects of green roofs and façades;
• identify suitable green-infrastructure configurations for selected building types;
• determine where interventions could provide the greatest benefits;
• evaluate performance under current and future climate conditions;
• balance energy, carbon, thermal and practical considerations through multi-objective optimisation.
The research will integrate building characteristics, weather records, energy data, land-use information and satellite-derived geospatial data. The likely methodology will combine building-performance simulation, machine learning, GIS, remote sensing and optimisation.
Building simulations will evaluate alternative green-roof and façade configurations. Machine-learning models will then predict performance efficiently across a wider range of buildings and climate scenarios. GIS and remote-sensing data will support the spatial assessment of suitable locations and environmental conditions.
The framework will be developed and evaluated using selected building types and case-study areas. The principal outcome will be an AI-enabled decision-support tool or framework to help building owners, designers, planners and policymakers select interventions that reduce operational carbon and improve resilience to overheating.
The project is primarily computational and does not require routine laboratory or fieldwork. Its final scope will be refined with the successful candidate to ensure that it remains achievable within the PhD period.
Candidate profile
Applicants should have, or expect to obtain, a good first degree (First Class or Upper Second Class) and a master’s degree with Merit or Distinction in building engineering, architecture, civil or environmental engineering, computer science, data science, geography or a related discipline.
Overseas applicants from non-English speaking countries must have an IELTS (International English Language Testing System) score of 6.5 overall, with no individual component scoring below 6.0.
Applicants should demonstrate quantitative and analytical ability, together with Python programming experience and experience in artificial intelligence or machine learning. They should also have experience in one or more of the following areas:
• GIS or remote-sensing analysis;
• building-energy or environmental modelling; or
• optimisation and statistical analysis.
Experience across all three additional areas is not required, but applicants should be willing to develop complementary skills during the project. An interest in low-carbon buildings, climate adaptation and urban sustainability is essential.
Funding and eligibility
Funding Full tuition fees and a tax-free stipend of £18,000 per year
Eligibility Home and International candidates
Proposed start September 2027
Duration 36
How to apply
Applications should be submitted through the apply button above.
Applicants must complete an online application form and provide: a research proposal outline prepared using the doctoral application template ; a current CV; a transcript of their highest qualification, and proof of English language ability for overseas applicants from non-English speaking countries.
For informal enquiries, please contact Professor Ali Bahadori-Jahromi at ali.jahromi@uwl.ac.uk . Full tuition fees and a tax-free stipend of £18,000 per year