高效多组分药物晶型筛选与晶体结构预测博士研究员
Efficient multicomponent pharmaceutical crystal form screening with crystal structure prediction
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 Chemistry (7189) • add name of the supervisor in section 2 of the application Applications should include: • 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
- • 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
- 本项目旨在开发用于快速筛选药物材料的新计算方法，结合晶体结构预测（CSP）、机器学习原子间势和开源软件开发，研究能否在无需对每个候选系统进行完整CSP研究的情况下识别出稳定的共晶或共晶形成倾向，并与阿斯利康（AstraZeneca）紧密合作以应对现实世界的固体形态挑战。
- 申请条件
- 申请者需持有化学、材料科学或紧密相关学科的英国2:1荣誉学位或国际同等学历；具备优秀的组织与沟通能力，能够独立及团队协作；有Python等编程经验者为佳。
- 待遇
- 提供针对英国及国际学生的资助机会（竞争性总统奖学金、学校奖学金等），通常涵盖英国标准学费及生活费津贴；提供晶体结构预测、分子模拟、机器学习、科学软件开发等多方面的专业培训。
- 申请方式
- 申请人需登录学校系统选择研究型（Research）2027/28学年、工程与物理科学学院、全职或兼职，搜索化学博士项目（PhD Chemistry 7189），并在申请表第二部分添加导师姓名。
- 材料清单
- 简历（CV）
- 2封学术推荐信
- 至今为止的学位成绩单与证书
- 英语语言资格证明（如适用）
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
- 化学
- 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）
- Graeme Day
- 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）
- 确认支持国际生全额资助
Fully funded (UK and international)
原文
View all current projects
Postgraduate research project
Efficient multicomponent pharmaceutical crystal form screening with crystal structure prediction
Funding
Fully funded (UK and international)
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
Create new computational methods for the prediction of materials structure to transform the development of medicines. Pharmaceutical scientists routinely screen large numbers of potential crystal forms to improve drug performance, but experimental screening is time-consuming and resource intensive. This project will develop new computational methods to rapidly screen pharmaceutical materials.
Many active pharmaceutical ingredients can be co-crystallised with another small molecule (coformer) to produce materials with improved solubility, stability or manufacturability. However, identifying which coformer will produce suitable crystal forms remains a major challenge. Existing computational screening approaches provide useful guidance but often do not explicitly assess crystal packing and solid-state stability. Crystal structure prediction (CSP) can provide this information but is currently too computationally expensive for routine large-scale screening.
In this project, you will develop accelerated CSP workflows that retain predictive accuracy while dramatically reducing computational cost. Working with newly developed methods and machine-learned interatomic potentials, you will create tools capable of screening large libraries of coformers. You will investigate whether stable cocrystals or the propensity to form a cocrystal can be identified without performing a complete CSP study for every candidate system.
The research combines computational chemistry, machine learning, crystal engineering and scientific software development. You will work with the open-source mol-CSPy software package that we develop in our research group, and will collaborate closely with researchers at AstraZeneca, gaining exposure to industrial pharmaceutical development and real-world solid-form challenges. You will also have opportunities for visits or placement activities with AstraZeneca.
The outcomes of this project will include new computational methodologies for pharmaceutical crystal screening, published research papers, and software tools that support the development of future medicines.
You will receive training in:
• crystal structure prediction and molecular simulation
• machine learning methods for molecular and materials modelling
• crystal engineering and solid-state chemistry
• scientific software development in Python
• high-performance computing
• scientific communication and presentation skills
• industrial pharmaceutical research through collaboration with AstraZeneca
The School of Chemistry & Chemical Engineering 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
Professor Graeme Day
Professor of Chemical Modelling
Research interests
• crystal structure prediction
• materials discovery
• computational chemistry
Entry requirements
A UK 2:1 honours degree, or its international equivalent, in one of the following:
• chemistry
• materials science
• or a closely related discipline
Essential skills:
• strong organisational and communication skills
• the ability to work both independently and as part of a team
Desirable skills:
• coding experience, preferably in Python
You should have an interest in developing methods for industrially relevant research and are expected to collaborate closely with AstraZeneca.
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 Chemistry (7189)
• add name of the supervisor in section 2 of the application
Applications should include:
• 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 Professor Graeme Day (G.M.Day@soton.ac.uk).