Initial training plan and internal competition

As we mentioned in the last post, because of the limits imposed by UKRI on the number of submissions allowed per institution and a desire to submit the highest-quality poroposals that we could, there was an internal competition to help filter out and focus support for proposal teams. In the last week we had to submit an internal EoI document that had some very focussed questions about our CDT plans. This document covered all aspects of how the CDT would be managed, what the training would be, what the core focus was, why there was a national need, etc. At one point the draft of our document spanned more than 15 pages, but with a lot of editing it was reduced down to 5 — good practice because the official EoI submission to UKRI had to fit on 3 pages!

Insights into our initial training plan

Perhaps the biggest question that needed to be answered in the document was what the training programme would look like. Using the case studies we had an idea about what kinds of things we wanted to offer as part of the programme. There were some discussions about the structure of the overall programme, choosing between a 1+3 integrated PhD with a MSc year, or a 4-year PhD with embedded training. We very quickly settled on the latter because it gives a much better value proposition to students who already have undergraduate masters degrees, allows more time for the research project (and collaboration with industrial partners), and is generally more flexible. There have also been some strong preferences for this choice from our industry partner survey. Southampton’s existing (regular) PhD regulations already contain sufficient allowances for compulsory assessed training to be embedded (e.g. such that a student must complete/pass certain elements to progress) and suitable exit routes in the unlikely event milestones not be achieved satisfactorily; this allowed us to design the training programme within the existing structures and not need to develop a custom programme.

Based on the case studies and input from potential industrial, governmental and NGO partners, our overall plan for training currently brings together a range of different activities over the course of a 4-year PhD and looks like this:

Cohort wide activities

Cohort wide activities which embed multidisciplinary skills are combined with specialised training tailored to address individual needs. The latter aspects will be identified by students and supervisors from a range of training courses provided by the CDT. Our training structure consists of three distinct elements: (i) subject specific training, (ii) research and enterprise skills training and (iii) research in practice training. Co-developing content with partners will ensure PhDs are equipped with knowledge to address current skills gaps. Students will be assessed through module requirements and as part of their annual review.

Subject specific training

We will recruit students with wide-ranging prior knowledge of Geospatial science and/or AI. To bring cohorts to the same level, students attend 3 compulsory Y1 foundation modules: Foundation of Geospatial Science, Foundations of AI, and Foundations of Geospatial AI. Further optional modules (identified through individual training plan) will develop either subject specific or software and data skills. Optional modules will be selected from the existing 100+ module catalogue covering both theoretical and applied aspects of Geospatial AI. Y2 and Y3 students will take at least one subject specific training module either offered at UoS through existing degree programmes or national training centres, and partner organisation such as NCRM & SEPnet.

Research and Enterprise skills

Our Researcher Development Programme (RDP) and Graduate Research Workshop (GRW) modules develop these skills. RDP: supervisors and students assess generic skills training need and identify at least 2 programmes each year through the Centre for Higher Education Practice (CHEP), Public Engagement with Research unit (PERu) and Public Policy at Southampton. These cover generic research skills, academic writing, publishing, knowledge exchange, and public engagement. GRW: We will develop a new module (24 1h sessions per year) which will address research and enterprise training needs that are not covered by RDP, with bespoke interactive workshops and networking events to cover technical and technological application aspects of Geospatial AI and facilitate peer-to-peer learning and interactions both within and across cohorts. Repetition will be avoided by developing a 3-year programme, focusing on application (Y1), commercialisations (Y2), entrepreneurship (Y3). Participation will be compulsory and assessed, with students maintaining a learning diary. As part of GRW, students will organise an annual conference to present their research to internal and external audiences. GRW will be offered to other doctoral students to facilitate cross-pollination of ideas.

Research in Practice

Research in Practice is achieved through: (i) Grand Challenges (GCs) and (ii) internship and (iii) public engagement. GCs: This annual event will be a key training activity to strengthen cohort building and develop generic skills needed to bridge theory and practice. GCs will be driven by a policy and/or industry need and will be set by external partners. Teams of 4-6 PhD students address a specific GC over a 1-week period using Geospatial AI techniques. During the initial CDT year, teams will be mentored by academics and partners. As we progress through the CDT, Y3 and Y4 students will be mentors. Internships: Each student will have an opportunity to be embedded at: the external industrial sponsor of the PhD topic, generic internship opportunities offered through project partners (e.g., ESA, OS) and leading international academic institutions with whom UoS have existing MoUs, for a period of up to 3 months. Public engagement: PhD students will receive training on public engagement from PERu and will engage in at least two outreach activities (e.g., local schools, public) to communicate AI and its role in our society.