Training is integrated into your degree programme for the full time that you are with us. In the early years you will take a selection of compulsory and elective choices to establish a solid knowledge base to best complement your interests, development needs, and to help you conduct innovative and productive research at the highest level. We also want to develop your broader skills that will help you now and in your future careers. This takes the form of non-assessed additional longitudinal training in aspects including ethics, responsible research innovation, entrepreneurship, team-working, presentation skills, and many others. This programme is flexible and will be reviewed and refined each year in partnership with students, staff, and external partners.
Assessed Training Courses
Year 2
Inter-Disciplinary Biomedical AI Research
(20 credits)
Case Studies in AI Ethics
(10 credits)
&
Elective Courses
(0-60 credits)
Year 3
Elective Courses
(0-30 credits)
Year 4
No assessed training
Placement Research Project (Years 1-3)
As part of your studies you will undertake a 3 month research project in collaboration with one of our partners. This could be in Edinburgh or in other locations depending on the project. This project can be taken once in any of the first three years of the programme. The project is written up as a dissertation worth 60 credits. We reserve year 4 so that students can concentrate on the completion of their research and writing up their PhD thesis.
Elective Courses
Elective course allow you to customise your formal learning with guidance from CDT academics and your supervisory team. The aim is to give you the freedom to explore new areas, fill skill gaps, and put you in the best position to undertake your research and pursue your desired future career. As our programme is integrated you have the flexibility to take elective courses at any point in years 1-3. We believe this is essential as new learning requirements are likely to emerge throughout the course of your studies. We have prepared a Degree Programme Table that contains a list of courses that you may choose to take and that we think are well aligned with the programme.
Applied Machine Learning (20 credits, INFR 11211)
Machine Learning and Pattern Recognition (20 credits, INFR 11130)
Machine Learning Practical (20 credits, INFR 11132)
Machine Learning Theory (10 credits, INFR 11202)
Probabilistic Modelling and Reasoning (20 credits, INFR 11134)
Reinforcement Learning (10 credits, INFR 11010)
Bayesian Theory (10 credits, MATH 11177)
Bayesian Data Analysis (10 credits, MATH11175)
Methods for Causal Inference (10 credits, INFR11207)
Statistical Programming (10 credits, MATH11176)
Software Development (10 credits, INFR 11172)
High Performance Data Analytics (10 credits, EPCC11014)
Advanced Database Systems (20 credits, INFR11199)
Extreme Computing (10 credits, INFR11088)
Natural Computing (10 credits, INFD11007)
Text Technologies for Data Science (20 credits, INFR11145)
Foundations of Natural Language Processing (20 credits, INFR10078)
Accelerated Natural Language Processing (20 credits, INFR11125)
Knowledge Graphs (10 credits, INFR11215)
Incomplete Data Analysis (10 credits, MATH11185)
Mathematical Biology (10 credits, MATH10013)
Biomedical Data Science (10 credits, MATH11174)
Next Generation Genomics (10 credits, BILG11004)
Bioinformatics Algorithms (10 credits, PGBI11057)
Linkage and Association in Genome Analysis (20 credits, PGBI11086)
Population Genetics (20 credits, PGBI11124)
Quantitative Genetics (20 credits, PGBI11125)
Modelling and Measuring Drug/Protein Interactions (20 credits, PGBI11131)
Biobusiness (20 credits, PGSP11331)
Innovation-driven Entrepreneurship-Data Science, Technology and Innovation (10 credits, CMSE11515)
Non-Assessed Training
Longitudinal Training (Years 1 – 4)
In addition to your assessed training the CDT programme includes a wide variety of other skills training and provides opportunities to gain other experience to help your development into an effective independent critical thinker with valuable transferrable skills.
Courses offered by the Institute for Academic Development (IAD) in research ethics and integrity; getting started with postgraduate research; writing skills; researcher coaching; and creating research impact. Students will be guided by our year timeline model of PGR transferable skills training developed by the Informatics Graduate School which includes project design; time management; writing; presenting skills; and thesis and exam preparation.
Responsible Research and Innovation (RRI) is a framework for reflecting on, anticipating, and deliberating about the ethical, social, and legal questions that arise in the research and development of scientific and technological tools, practices, and systems. Throughout your time in the CDT you will learn how to apply this framework to your research and ensure that it creates value for society in an ethical and responsible way.
You will be trained in RRI by our expert training lead throughout your time in the CDT. RRI forms part of our Approach to Responsible AI and will include core RRI material co-developed with the Alan Turing Institute and support from Responsible AI UK adapted with biomedical specific case studies. We were the first UK university to collaborate with ATI on RRI training. The course covers: Understanding Responsibility; Collective & Distributed Responsibility; Scope of Responsibility; Project Lifecycle; and SAFE-D Principles (Sustainability, Accountability, Fairness, Explainability and Data Stewardship).
This will be complemented by modules from the skill track on Ethics & Governance: Practical Ethics; CARE & ACT Principles; Stakeholder Engagement; and AI Regulation.
RRI/Ethics content will be delivered flexibly through self-study, masterclasses, and an ‘AI Project Lifecycle’ workshop where you will discuss with your CDT peers RRI/Ethics issues arising from their research. RRI will be continuously embedded in your project lifecycle, including the requirement for an RRI plan to be incorporated in your detailed project proposal and updated each year as part of the annual review process.
Over the past four years UoE have accelerated 280 ventures helping to raise >£150m and 150 UoE student businesses to raise >£30M in the last year alone. They will convene a network of investors, researchers, and industry partners and provide support to ensure long-term success for our entrepreneurial activities. The Bayes Centre and Edinburgh Innovations (EI) will provide mentoring, business advice, incubation, entrepreneurial courses, acceleration, and a rich network of contacts. You will have access to EI’s exciting PhD Max programme designed to empower entrepreneurial PhD students and the Venture Builder Incubator (VBI) a four-month digital transformation programme providing you with the skills to create and grow science-based start-ups that positively impact society. Together these will provide excellent opportunities for you to maximise the impact of your research, engage with external partners, and translate ideas into real-world usage.
The programme is co-created with our external partners to ensure that you become innovation ready graduates, that your research is driven by stakeholder needs, and to maximise its impact. To facilitate this, our dedicated Business Development Executive and Entrepreneurship Training Lead will work closely with you to support and develop your innovation and entrepreneurship goals.
You will take part in regular hackathon enterprises where you will work collaboratively on data and coding challenges. These will include competitions (e.g., DREAM Challenges and BioCreative), industrially sponsored ones (e.g., Roche Dementia Challenge), and ones proposed by academics, other students, and partners. These will strengthen the connection with your peers, enable peer-peer learning, and hone software engineering, data handling, and analysis skills.
You will benefit from our seminar series and other talks that will be a blend of traditional academic talks, career exemplar vignettes from alumni and early career staff from academia and beyond, and skills development/awareness talks. The programme will host two distinguished lectures per year, and a mixture of other local, national, and international speakers from academia and external partners.
You will receive training in public communication and engagement through IAD courses, ATI Turing Commons, Vox Coaching, and by CDT management group members. We will support you to develop podcasts, instructional videos, blogs, and use of social media to communicate your research. You will take part in outreach activities (e.g., the Edinburgh Science Festival, and Sutton Trust workshops to widen participation in higher education) as well as in local and national health innovation networks (e.g., the Scottish Health and Social Care Innovation Network, One HealthTech UK, and UoE Medical School innovation network, coordinated by the Usher Institute).
We will co-ordinate activities to create open courseware using models developed by the Turing Commons, and The Turing Way at the ATI, Edinburgh Carpentries, and the Ed-DAsH team. You will work with faculty and external partners to co-develop educational and informational open-source material to establish a bank of resources for different stakeholders to engage with biomedical AI research and its implications for society.
You will benefit from our extensive experience preparing PGR students for life after their PhDs. We will host seminars and visits by people from different career stages from public and private sector organisations, including our external partners, focussing on career journeys. These “case-studies” will give students examples of career options, and provide opportunity to discuss decisions, challenges, and opportunities with presenters.
You will also have access to the University Careers Service and advice and mentoring from academics and staff from external partners.
All students, supervisors, and CDT management group members will have the opportunity to take mental health first-aid training with Digital Bricks.