Project Opportunities
For summer 2025, we are looking to recruit three interns to join the ICCS team. Two will work on research projects under the supervision of an ICCS postdoctoral researcher and the third will work with the ICCS Programme Manager in a Science Communications role.
- Communications for the Institute of Computing for Climate Science
- Prediction of the Evolution of Forest Fire and Associated Impacts
- Fluid: A Programming Language for Transparent, Self-Explanatory Research Outputs
- Centre for Landscape Regeneration - Data Engineering of Heterogeneous Datasets: Exploring relationships in environmental research
Communications for the Institute of Computing for Climate Science
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Project Title |
Communications for the Institute of Computing for Climate Science |
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Keywords |
science communication, computer science, climate science |
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Project Supervisor |
Josh Stevens, ICCS Programme Manager |
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Contact Email |
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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 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 starting new initiatives. You will also assist with the annual Summer School that the institute will be hosting for PhD students and post-doctoral researchers in July 2025. |
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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 in our Summer School. |
| Essential knowledge, skills, and attributes | No climate related knowledge is expected but a strong interest is desirable. |
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References |
Communications, Networks and Branding at ICCS | Institute of Computing for Climate Science |
Prediction of the Evolution of Forest Fire and Associated Impacts
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Project Title |
Prediction of the Evolution of Forest Fire and Associated Impacts |
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Keywords |
Forest fires, climate, machine learning |
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Project Supervisor |
Robert Edwin Rouse |
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Contact Email |
rer44@cam.ac.uk |
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Background Information |
The amount of forest cover lost due to forest fires has increased by over 6 million hectares since 2001, with the proportion of all forest cover loss increasing from 20% to 33%. The conditions |
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Project Description |
Building upon existing data driven methods, this project will focus on developing proxy variables for vegetation condition and forest fire ‘fuels’ by assimilating a range of satellite and meteorological data within a machine learning pipeline. This will then be compared against existing indices with a view to creating a more accurate forest fire early warning and long range predictive tool. |
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Work Environment |
Part of a team with Postdocs, MPhil students, and a CAT research team at an insurance company. |
| Essential knowledge, skills, and attributes | Thermodynamics, Plant Biology, Statistics |
| Other skills used in the project | Geophysics, Machine Learning |
| Acceptable programming languages | Python (PyTorch/numpy) |
| References |
Aldersley, A., Murray, S.J., Cornell, S.E., 2011. Global and regional analysis of climate and human drivers of wildfire. Science of The Total Environment 409, 3472–3481. https://doi.org/10.1016/j.scitotenv.2011.05.032 |
Fluid: A Programming Language for Transparent, Self-Explanatory Research Outputs
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Project Title |
Fluid: A Programming Language for Transparent, Self-Explanatory Research Outputs |
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Keywords |
programming languages; machine learning for software engineering; climate science; open science; data visualisation |
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Project Supervisor |
Dr Roly Perera |
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Contact Email |
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Background Information |
This is an exciting opportunity to work on programming language design and AI tooling for data science, in the context of a new programming language called Fluid being developed at the Institute of Computing for Climate Science in Cambridge (ICCS). Fluid is a new kind of “transparent” programming language that can be used to create charts and figures which are linked to the underlying data. This allows a curious reader to discover what visual elements actually represent by interacting with the chart. See our demos at https://f.luid.org and our poster at https://f.luid.org/fluid-poster.pdf for more of an idea. |
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Project Description |
There are two novel applications driving the next iteration of Fluid. Both lie at the intersection of programming languages research, AI and human-computer interaction (HCI): 1) Extending Fluid with *computational explanations*: information about the specific steps that were involved in a computing a particular feature of the output (e.g. the whiskers decorating a bar in a bar chart). This a potentially powerful transparency feature, allowing readers (perhaps during peer review) to discover otherwise hidden or obscured facts about the data underpinning a visualisation. One internship project will involve turning these computational explanations into more user-friendly natural language explanations that would be useful for lay readers as well as expert readers. The novel contribution that Fluid can make to this problem is to provide an authoritative ground truth for the generation of the natural language, offering the prospect of a “trusted” or reliable form of open, self-explanatory artifact. (See “AI reading assistant” in the poster.) 2) Extending Fluid with *transparent text*: natural language (such as the expository text in a climate report for policy makers) which is underwritten by a semi-formal computational interpretation, especially quantitative phrases or other fragments of text expressing data-driven claims. For example, the statement that under a particular emissions scenario, global warming is _extremely likely_ to exceed 2°C in the 21st century can be underwritten by a Fluid program that assigns a specific interpretation to this text in terms of the distributions of the underlying data used (by the report author) to reach that conclusion. Another internship project will be to develop AI tooling which replaces fragments of text by expressions that compute that text from data. (See “AI authoring assistant” in the poster.) This will be an opportunity for a strong, scientifically-minded student to learn more about the application of computer science to problems in open science and science communication. ICCS will provide careers advice and networking opportunities for students interested in working at this intersection. Successful applicants can expect to gain experience in programming language implementation, AI tooling for software engineering, the mapping between formal languages and natural language, and data visualisation. |
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Work Environment |
The intern will work in close (daily) collaboration with the PI and will be encouraged to present their work to researchers and data scientists at the ICCS, the Department of Computer Science and Technology (Cambridge), and collaborators at The Alan Turing Institute and University of Bristol. |
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References |
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| Essential knowledge, skills and attributes | A strong background in at least one of computer science, maths and/or natural sciences is required. |
Centre for Landscape Regeneration - Data Engineering of Heterogeneous Datasets: Exploring relationships in environmental research.
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Project Title |
Centre for Landscape Regeneration - Data Engineering of Heterogeneous Datasets: Exploring relationships in environmental research |
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Keywords |
Database, Metadata, ETL, Data Relationships, Data Accessibility |
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Project Supervisor |
Simon Sadler, Research Software Engineer, University Information Services |
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Contact Email |
ss3272@cam.ac.uk |
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Background Information |
The Centre for Landscape Regeneration (CLR) is led by the University of Cambridge with NIAB, RSPB, the Centre for Ecology and Hydrology (CEH), and the Endangered Landscapes and Seascapes Programme (ELSP). [1] The CLR is providing knowledge and tools necessary to regenerate the British countryside using cost-effective nature-based solutions. By harnessing the power of ecosystems, we strive to deliver broad societal benefits, alongside biodiversity recovery, climate mitigation and adaptation. Collaboration with farmers, practitioners, communities, policymakers, and industry is key to this work. The CLR was launched in 2022 to deliver a programme of research to understand and regenerate iconic UK landscapes, ensuring a sustainable future for people and nature in these landscapes. The CLR is supported by funding from UKRI NERC, as part of their Changing the Environment Programme. [2] |
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Project Description |
The CLR made significant progress on the research in the Cambridgeshire Fens, Scottish Cairngorms and in Cumbria, with extensive fieldwork and data collection including biodiversity, greenhouse gas emissions, socio-economic studies, policy assessment and soil composition analysis amongst other areas. Data management and data accessibility is key to the success of the interdisciplinary approach of the CLR. A data survey highlighted 50 file groups across 19 file formats including geospatial, spreadsheets and images. The project will explore these heterogeneous datasets to find relationships and convergence, ultimately importing data into a single repository to help with future analysis. You are free to explore the data as appropriate. You will communicate your findings and enter discussions to determine database selection and API access. You will then assist with data imports. There may be opportunities to help with aspects of data analysis and visualisation subject to time constraints. Your work will smooth the path for analysis that will shape the best outcomes and decisions. This project is an opportunity to work with real data at a crucial stage in the research pipeline. |
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Work Environment |
The project will be conducted individually with regular supervision and access to specialists upon request. The workplace is a flexible hybrid environment, office space is available 5 days a week at the Roger Needham Building on the West Cambridge site, but there is no expectation to attend everyday (unless this is the student's preference). Project meetings will take place with supervisors both in person and remotely. |
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References |
[1] https://www.clr.conservation.cam.ac.uk/ [2] https://www.changingtheenvironment.org/ |
| Essential knowledge, skills and attributes | Data Engineering, Geospatial data, Databases, ETL |
| Other skills used in the project | Communication of ideas, Data Access APIs |
| Acceptable programming languages | General Database, Python |