CDT
Our new EPSRC funded Centre for Doctoral Studies (CDT) based in the University of Cambridge offers a four-year PhD programme which will train students from a range of backgrounds to think differently and creatively about making molecules like new drugs by combining state of the art chemical synthesis with the latest developments in machine learning and artificial intelligence.
SynTech
SynTech is short for Automated Chemical Synthesis Enabled by Digital Molecular Technologies. The aim of the CDT is to bring together a diverse group of chemists, chemical engineers, theoreticians and data-scientists of all backgrounds, to create a broad understanding of how we can accelerate and improve the molecule-making process. This is a postgraduate inter-disciplinary training programme in which students will train at the interface of these areas to develop the skills, knowledge and confidence to tackle today's evolving issues.
Why SynTech
Despite tremendous advances in synthesis, the increasing challenges posed by devising new compounds of potential interest for drug and agrochemical discovery and development continues to stretch synthetic chemists. There is a recognised need across the pharmaceutical, biotech, agrochemical fine chemicals and small-molecule functional materials sectors to dramatically shorten synthesis timeframes and improve productivity and cost effectiveness. The world needs researchers who can combine new technologies like automated synthesis with data-driven science such as machine learning methods and artificial intelligence. The programme will enable each student to become an expert in a chosen field related to the CDT's scientific aims, as well as equipping you with a broad set of complementary scientific skills, education in sustainable chemistry and ethical training.
Overall Course Structure
In your first six months you will have intensive training through our Core Skills Training Phase as part of the compulsory elements of the course. This will set you up to write a guided group PhD proposal and begin further courses in your area of interest.
The training programme consists of workshops, experimental research experience and bespoke taught courses broken down into two distinct types:
​
-
COMPULSORY ELEMENTS which will be followed by all students during the first six months including
-
Focused bridging courses, tailored to student needs;
-
Guided small-group training projects selected from a list hosted in Department of Chemistry or other partner Departments.
-
OPTIONAL ADVANCED COURSES, which will be followed by students according to individual interest/need and at any time during their PhD:
-
Advanced academic courses tailored to individual needs;
-
Researcher development/transferable skills sessions.
-
We have laid out an overview of the four-year structure below highlighting all training, research and assessments involved.
These courses are largely tried, tested and optimized courses that we would expect all CDT students to take, in order to underpin all aspects of their experience in the CDT. Many of them have already been piloted, some for a number of years, and we are confident that they will provide our CDT students with an excellent foundation upon which to build their PhDs, whatever their area of specialization. All courses will be taken as part of the first year experience that students have in our CDT.
Students in the CDT will be drawn from a broad range of training backgrounds and disciplines. They will necessarily need to attend bridging courses, which will be aimed to give them a good understanding of all areas covered by our CDT. Some of these will be drawn from existing Tripos or MPhil courses and some will be entirely new courses written to underpin the student knowledge base within our CDT.
Essentially, the CDT aims to train:
-
chemists in cheminformatics, computational parameterization, modelling and ML & AI concepts so that they understand clearly how best to work together with people skilled in the complementary areas of the SynTech-CDT.
-
theoreticians and data-scientists in understanding the challenges & demands of synthetic chemistry & process engineering and how ML & AI solutions might be realized from partnership with experts to solve challenging problems.
-
chemical engineers in understanding the practical challenges provided by synthetic chemistry & the difficulties they need to consider when, for example, translating synthesis to robotic systems, as well as how synthetic chemists & theoreticians might work with them to design experiments and facilitate these processes.
