机器人群体自适应集体决策博士研究员
Adaptive collective decision-making in robot swarms
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
- 2027-08-31
- 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
- 本项目研究动态环境中的自适应与分布式集体决策，使机器人群体能够动态确定谁应参与、需要多少信息以及何时随着新证据的出现改变决策。研究方向包括自适应群体子集、动态共识、信息驱动的参与、不确定性感知的决策、不断变化的环境条件以及相互竞争的群体目标之间的冲突，并将利用SooratiLab中的机器人平台（如机器狗、地面巡视车、四旋翼飞行器、机械臂等）通过模拟和物理多机器人平台开发和评估算法。
- 申请条件
- 申请者必须持有计算机科学、机器人学、人工智能、工程学或紧密相关学科的英国2:1荣誉学士学位或其国际同等学历。具备对自主系统、机器人学或人工智能的强烈兴趣，能够独立开展研究并批判性评估科学文献，以及具备良好的编程技能（优选Python或同类语言）。
- 待遇
- 学校为英国和国际学生提供一系列资助机会，包括地平线欧洲学费减免、总统奖学金及各学院提供的竞争性奖学金（通常涵盖英国本土学费及生活津贴）。
- 申请方式
- 申请人需通过学校在线系统申请2027/28学年工学与物理科学学院的全日制或兼职博士计算机科学（7089）项目，并在申请表的第二部分添加导师姓名。资助以滚动方式颁发，截止日期为2027年8月31日。
- 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与证书 (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
- 2027-08-31
- 学科
- 计算机科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Dr Mohammad Soorati
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-10
- 详情核验
判定依据（原文摘录）
- is_phd
Type of degree Doctor of Philosophy
- bachelor_ok
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
- supervisor_name
Lead supervisor Dr Mohammad Soorati
原文
View all current projects
Postgraduate research project
Adaptive collective decision-making in robot swarms
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
31 Aug 2027
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
How can a robot swarm make good collective decisions when its environment is constantly changing? This project investigates adaptive and decentralised approaches to collective decision-making, enabling robot swarms to dynamically determine who should participate, how much information is needed, and when decisions should change as new evidence emerges.
Autonomous robot swarms must often make decisions collectively: where to search, which target to investigate, which route to follow, or how to allocate resources. In real environments, however, information is incomplete, conditions change and decisions that were appropriate moments ago may quickly become outdated. Requiring every robot to participate in every decision can also waste valuable resources and prevent robots from performing other tasks.
This project investigates adaptive collective decision-making in dynamic environments, building on recent work in subset-based collective decision-making. The research explores how a swarm can dynamically determine which robots should contribute to a decision, how much information is required, and when an existing collective decision should be revisited.
The project investigates decentralised approaches in which robots make decisions using local information while collectively achieving robust global behaviour. Potential research directions include:
• adaptive swarm subsets
• dynamic consensus
• information-driven participation
• uncertainty-aware decision-making
• changing environmental conditions
• conflicts between competing swarm objectives
You'll develop and evaluate algorithms using simulation and physical multi-robot platforms. Methods may include distributed algorithms probabilistic decision-making, swarm intelligence and online adaptation. The research will contribute fundamental knowledge about how collective intelligence can remain robust and responsive when both the swarm and its environment are changing. Applications could include search and rescue, environmental monitoring, exploration, autonomous logistics and other complex multi-robot missions. You'll have access to the robotic platforms available in SooratiLab including robot dogs, several ground rovers, quadcopters, robotic arms, and more.
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 Mohammad Soorati
Dr.Eng., MSc, BSc
Associate Professor
Research interests
• Human-swarm Interaction
• Multi-robot Systems
• Swarm Robotics
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
• computer science
• robotics
• artificial intelligence
• engineering
• a closely related discipline
Essential skills:
• strong interest in autonomous systems, robotics or artificial intelligence
• ability to undertake independent research and critically evaluate scientific literature
• good programming skills, preferably in Python or a comparable language
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 Mohammad Soorati (M.Soorati@soton.ac.uk).