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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training
 

MRes - Year 1 - taught component

Introduction

The Induction week is a key, mandatory, element to the first year. Taking place outside of term time (for 2020 this will be the 28 September – 2 October) the induction will introduce students to the main concepts of the programme and will include a mixture of practicals, lectures, team building and social activities.

Foundation

The compulsory Foundation course will run through Michaelmas Term (October-December) and Lent Term (January-March) of the academic year. It consists of a set of mandatory lecture courses supported by examples/tutorial classes and practicals, and a guided team challenge. Lecture courses may include Probabilistic Machine Learning, Fundamentals of probability, statistics and programming, Environmental Risks from a systems perspective, Cloud Computing. Environmental Data Analysis. Confirmed content will be advised during the induction week.

Guided Team Challenge: students will compete in two teams to construct a data-driven evidence base in support of an environmental challenge. The project will start in December and run through until March, with guided activities to enable the students to access and process data and to use AI approaches to generate decision-support information. This activity will be formally assessed by way of a report and presentation.

Specialisation

Students will choose at least two lecture courses, one from an “Application Domain” list, the other from a programme selected list, chosen specfically to allow students to tailor their training according to their individual research interests and backgrounds.

Examples of current courses:

Application Domain:

  • Atmospheric chemistry & global change
  • Climate change and the carbon cycle
  • Fluid Dynamics of Climate
  • Responses to Global Change
  • Natural Hazards

Other relevant course include:

  • Mobile Robot Systems
  • Advanced Topics in Machine Learning and Natural Language Processing
  • Machine Learning and Bayesian Inference
  • Statistical Learning in Practice
  • Inference
  • Advanced Machine Learning

MRes research project

A list of agreed projects will be circulated to choose from, however, there is also scope for students to secure their own project. Students must discuss this with their first year supervisor, before committing to anything, to ensure projects are suitable and relevant to the programmes aims and objectives. Students will start their project in March/April and will be formally assessed by a research report and presentation.

Professional Development

Various compulsory one-day professional development workshops will be held throughout the first year which will include keynote lectures, group activities and discussions. Additional training and seminars will also be held which will be directly relevant to both the programmes themes, and to you as a researcher and student.

PhD - Years 2-4

After the first year, the programme will continue to hold training, seminars, conferences and workshops to build on the knowledge provided during the MRes. This will be in addition to the practical training received as a PhD student.