Projects

Project Title

Communications for the Institute of Computing for Climate Science

Keywords

science communication, computer science, climate science

Project Supervisor

Marla Fuchs, Executive Director of the ICCS

Contact Email

iccs@maths.cam.ac.uk

Project Description

We are looking for an enthusiastic science communicator to support creating new scientific media content to inform and inspire public audiences about climate science and the role of software engineering, computer science, artificial intelligence, and data science within climate science. 

You will help the public audience understand highly technical information and concepts, play an integral role in expanding the online presence of the newly established Institute of Computing for Climate Science at the Faculty of Mathematics, and have a range of opportunities to work with senior academics in the field. 

Some examples of what you will be working on include the institute website and blogs, social media and YouTube channel, with the possibility of even starting a podcast. You will also assist with the annual Summer School that the institute will be hosting for PhD students and post-doctoral researchers in July 2024.

Work Environment

The successful candidate will be part of the Operations team at ICCS, directed by Marla Fuchs. They will share an office in Pavilion H of the Centre for Mathematical Sciences in Cambridge (CMS) with the Programme Manager and Programme Administrator. The students will have the opportunity to interact with the Directors, Researchers, and Research Software Engineers that are part of ICCS. They will also liaise with other academics and staff in the Department of Applied Mathematics and Theoretical Physics (DAMTP), the Department of Computer Science and Technology and University Information Services (UIS), as well as the wider University and international researchers that form the VESRI community and take part of our Summer School.

References

Communications, Networks and Branding at ICCS | Institute of Computing for Climate Science

Project Title

Automatic Climate Scientist

Keywords

Statistics, Machine Learning

Project Supervisor

Dr Henry Moss

Contact Email

hm493@cam.ac.uk

Background Information

There is a significant disconnect between modern ML methods and their ability to assist climate scientists in gaining deeper scientific understanding from observational data. Specifically, the challenge lies in distilling the fundamental equations governing physical phenomena. Indeed, this goal is directly at odds with the black-box nature of recent ML advancements, which prioritise raw predictive performance over interpretability. Therefore, to realise the potential of ML within climate science, we need to shift our focus to a fundamentally different methodology, namely equation discovery. Equation discovery produces a human-readable formula that can be easily trusted, understood, and verified by domain experts, setting it apart from even the most explainable black-box methods.

Project Description

The successful applicant will apply probabilistic ML methodology for discovering the governing equations of physical systems from noisy and sparse measurement data. The student will become an expert in Bayesian ML (e.g. Gaussian processes) and gain practical knowledge of fluid and atmospheric dynamics while applying their methodology in tandem with climate scientists. The exact climate systems used to test new methodology during the project will be adapted based on the student’s interests and background. A key part of the project will be in building open-source tooling to enable the use of the project’s outcomes by the wider climate community.

Work Environment

Centre for Mathematical Sciences, University of Cambridge

Project Title

Fluid: A Programming Language for Transparent, Self-Explanatory Research Outputs

Keywords

programming languages; program analysis; climate science; transparency; data visualisation

Project Supervisor

Roly Perera

Contact Email

roly.perera@cl.cam.ac.uk

Background Information

Charts and other visual summaries, curated by journalists and scientists from real-world data and simulations, are how we understand our changing world and the anthopogenic sources of that change. But interpreting these visual outputs is a challenge, even for experts with access to the source code and data. Fluid (f.luid.org) is a new “transparent” programming language, being developed at the Institute of Computing for Climate Science in Cambridge, that can be used to create charts and figures that are linked to data so a user can interactively discover what visual elements actually represent. This is an exciting opportunity to work on a new programming language designed to make climate science more open, intelligible and accessible.

Project Description

Fluid works by incorporating a bidirectional dynamic dependency analysis into its runtime, allowing it to track dependencies as outputs (such as charts and tables) are computed from data. It uses this information to automatically enrich rendered output with interactions that allow a reader to explore the relationship to data directly through the artefact, by selecting visual features of interest. Fluid uses so-called “program slicing” techniques based on Galois connections, a neat mathematical abstraction which characterises exactly the relationship between sets of inputs and sets of outputs which depend on them.

Your internship could go in a number of directions, depending on your interests. A programming languages project would extend Fluid into a literate programming tool, by adding Markdown support and the ability to embed computational content via a Lisp-style backquote mechanism. A more mathematical project might add multidimensional arrays to the language, along with various array operations inspired by linear algebra and an extension of the dependency analysis to these new operations. A project focused more around science communication would use Fluid to adapt a piece of real-world climate science into a “long-form” essay or interactive explanation (see distill.pub for some examples) intended for a non-specialist audience.

Whatever form your internship takes, we would aim for your work to be incorporated into our research software codebase and project outputs, and so would form a genuine contribution to an exciting new direction in programming languages. You will get to present your work to researchers and data scientists at the Institute of Computing for Climate Science and The Alan Turing Institute, and work with PhD students at Cambridge and Bristol. A strong background in functional programming, maths and/or science is a must, and you can expect to gain experience in programming languages research, data analysis and data visualisation.

Work Environment

The applicant will be part of a small team based in Bristol and Cambridge. There will be a PhD student in both locations able to provide support. Normal office hours apply, but remote working (in the UK) might be a possibility for a suitable candidate as long as they are prepared to make regular visits to Cambridge (we will cover travel and accommodation costs)

References

f.luid.org

Prerequisite Skills

Strong programming skills