国防工业中的合成自动化

本项目旨在开发一体化自动平台，快速合成同位素标记化合物，用于国防相关的分析和毒理学应用。项目结合合成化学、自动化和预测建模，优化合成流程并提高效率。候选人应具有化学或相关学科背景。

About the project This project will develop an integrated, automated platform for rapid synthesis of isotope-labelled compounds for defence-relevant analytical and toxicological applications. Combining synthetic chemistry, automation, analytical measurement and digital optimisation, it will investigate closed-loop isotope labelling for producing analytical reference standards and compounds supporting metabolism, toxicokinetic and chemical-threat studies. Isotope-labelled compounds are essential tools for chemical analysis, enabling the accurate identification and quantification of chemicals and their transformation products. However, their synthesis can be time-consuming and often requires considerable manual optimisation. This project will address this challenge by developing an integrated, automated platform for the rapid synthesis of isotope-labelled compounds for defence-relevant analytical and toxicological applications. The project will combine synthetic chemistry, laboratory automation, analytical measurement and digital optimisation to investigate closed-loop approaches to isotope labelling. Automated experimentation will enable reaction conditions to be rapidly explored and optimised, while simultaneously generating large, structured datasets of isotope-labelling reactions. These data will be used to develop predictive approaches that relate reaction conditions and molecular structure to labelling outcomes, ultimately enabling the planning of synthetic pathways to isotope-labelled compounds. Target compounds will include analytical reference standards and molecules supporting studies of metabolism, toxicokinetics and chemical threats. A distinctive feature of the project is its interdisciplinary nature. You will receive training spanning organic synthesis, isotope chemistry, automation, analytical chemistry, large-scale chemical datasets, predictive modelling and 3D printing, developing an unusually broad combination of practical laboratory and digital skills. The datasets generated will also contribute to the EPSRC-funded Physical Sciences Data Infrastructure (PSDI) data collection, allowing high-quality experimental data to be reused and integrated into other researchers' workflows. By connecting automated experimentation, predictive modelling and data sharing, the project aims to establish a more efficient and data-driven approach to designing and producing isotope-labelled molecules.

Entry requirements You must have an undergraduate degree (at least UK 2:1 honours degree) or its international equivalent in chemistry or a related discipline. Familiarity with, or experience in, any combination of synthetic chemistry, digital methods, laboratory automation or 3D printing would be useful, but applicants are not expected to have experience in all of these areas. Applicants with experience in one or more of these areas and an interest in developing skills across others are encouraged to apply. The project will provide opportunities to develop skills in: synthetic chemistry digital methods automation 3D printing alongside the wider training provided through the CDT

Funding: Competition funded Fees and funding For candidates looking to complete a PhD or EngD full-time we offer an enhanced tax-free stipend of up to £30k per year, along with a substantial budget for research, travel, and centre activities. CISDnS is committed to promoting equality, diversity and inclusivity. 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 or are transitioning into a new role. 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.

How to apply Please apply via the online portal and select: Academic year: 2027/28 If you will be full time or part time Faculty: Engineering and Physical Sciences Search for "complex integrated systems" Select either PhD or EngD – if you are unsure, select PhD Add the 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