人工智能驱动的信息物理系统的不确定性感知调度与分析博士研究员
Uncertainty-aware scheduling and analysis of AI-powered cyber-physical systems
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。
博士招生与资助公告
- 申请年度
- 2027；原帖说明：How to apply Apply now You need to: • choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences • select Full time or Part time • search for programme PhD Computer Science (7089) • add name of the supervisor in section 2 of the application Applications should include: • research proposal • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2026-12-11
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students. Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants. Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. For more information, please visit our postgraduate research funding pages.
- 资助条件
- 原帖为竞争性或附条件资助说明，未确认本项目获资助及申请人能获奖；不作为保证全奖。
- 原帖材料说明
- • research proposal
- • your CV (resumé)
- • 2 academic references
- • degree transcripts and certificates to date
- • English language qualification (if applicable)
确定性信息来自对应版本的完整原帖。原帖只提供日期，日末和学校当地时区为本站转换假设。筛选使用最后申请截止；资助与录取以学校审核结果为准。
AI 中文速览
- 研究内容
- 本项目旨在开发明确考虑不确定性的调度算法和分析方法，以构建更具可信度且资源高效的人工智能驱动信息物理系统（如汽车、飞机、机器人）。研究将结合数学建模、概率与统计、算法设计和实验评估，重点研究如何表征数量的不确定性、其依赖关系如何影响系统行为，以及调度决策如何在满足安全要求的同时提高效率。
- 申请条件
- 申请人需持有英国 2:1 荣誉学位或其国际同等学历，专业背景包括计算机科学、工程学、数学、机器学习（ML）或人工智能（AI）、物理学、电子学或相关学科。同时要求申请人对数学、统计学、概率论以及人工智能/机器学习技术中的至少两项充满热情。
- 待遇
- 提供针对英国及国际学生的多种竞争性资助机会（如学校提供的竞争性奖学金通常涵盖英国本土学费及生活费津贴、地平线欧洲学费减免或总统奖学金可覆盖国际生与本土学费差额），并支持参加国际会议以及发展技术和通用技能。
- 申请方式
- 申请人需在申请系统中选择研究型项目类型（2027/28学年，工程与物理科学学院）、全职或兼职、搜索PhD Computer Science (7089)项目，并在申请第二部分添加导师姓名后通过官网提交。
- 材料清单
- 研究计划书 (research proposal)
- 简历 (CV / resumé)
- 2封学术推荐信 (2 academic references)
- 迄今为止的学位成绩单和证书 (degree transcripts and certificates to date)
- 英语语言资格证明（如适用） (English language qualification (if applicable))
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 最终轮次截止
- 2026-12-11
- 学科
- 计算机科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Dr Filip Markovic, Professor Geoff Merrett
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-24
- 详情核验
判定依据（原文摘录）
- english_ok
English language qualification (if applicable)
- bachelor_ok
A UK 2:1 honours degree, or its international equivalent
原文
View all current projects
Postgraduate research project
Uncertainty-aware scheduling and analysis of AI-powered cyber-physical systems
Funding
Competition funded
Competition funded
View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
2:1 honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date
11 Dec 2026
On this page
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
AI and machine learning are increasingly used in cars, aircraft, robots, and other critical systems, but their performance, correctness, energy consumption, and safety are intertwined in ways that are hard to predict. We seek to untie this knot so we can build intelligent systems that are more trustworthy and resource-efficient.
In brief: How can modern cyber-physical systems (e.g., cars, aircraft, robots) make the right decision at the right time, without draining their batteries, while being safe to operate?
In addition, such systems increasingly rely on AI and machine learning, where computational performance, correctness, energy consumption, and safety are tightly connected. Consider the following simplified problem: A more accurate computation may require more time and energy, while a faster one may produce less reliable results. How to decide which one to use?
This challenge is already visible in space. For instance, NASA’s Perseverance rover adapts its onboard schedule to uncertain durations of science and engineering activities, which also make energy use difficult to predict. Such uncertainty can be particularly problematic if misjudged (a system can become inoperable, inefficient, unsafe, or even waste significant amount of taxpayer's money).
This project seeks to develop scheduling algorithms and analysis methods that explicitly account for uncertainty. Scheduling determines when computations run and how they share resources. Analysis assesses what these choices mean for performance, correctness, energy use, and safety. You will investigate how uncertainty in these quantities can be characterised, how their dependencies affect system behaviour, and how scheduling decisions can improve efficiency while respecting safety requirements.
The research will combine mathematical modelling, probability and statistics, algorithm design, and experimental evaluation. The aim is to provide both theoretical foundations and practical methods for intelligent systems that make better use of limited resources and offer clearer evidence of trustworthy behaviour.
You will be based and trained in the Cyber Physical Systems group in the School of Electronics and Computer Science.
You will be supported to attend international conferences and develop a wide range of technical and transferable skills.
If you feel you have gaps in some areas that might be required for the research, you will be able to attend any lectures across the wide range of courses offered within the School of Electronics and Computer Science to supplement your knowledge, for instance, AI, scheduling theory, computer science, embedded systems.
The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Potential supervisors
Lead supervisor
Dr Filip Markovic
Lecturer in Computer Science
Research interests
• Cyber-Physical Systems
• Real-Time Systems
• Scheduling Theory
Supervisors
Professor Geoff Merrett
PhD, BEng, PGCert, FHEA, SMIEEE, MIET
Professor
Research interests
• Energy management of mobile/embedded systems
• Self-powered computing
• Internet of Things
Entry requirements
A UK 2:1 honours degree, or its international equivalent, in one of the following:
• computer science
• engineering
• mathematics
• Machine Learning (ML) or Artificial Intelligence (AI)
• physics
• electronics
• or a related discipline
We are looking for applicants with a passion for at least two of the following: mathematics, statistics, probability theory, and AI or Machine Learning, technology.
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
Apply now
You need to:
• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Computer Science (7089)
• add name of the supervisor in section 2 of the application
Applications should include:
• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Dr Filip Markovic (f.markovic@soton.ac.uk).