Internship scheme - Summer 2025

The National Capabilities for Global Challenges - internship scheme 2025 

Applications for the 2025 internship scheme are now OPEN 

We are excited to be recruiting eight interns for our National Capabilities for Global Challenges internship scheme! Eight different research internships are available and you can apply for more than one. 

Through this programme, you'll delve deep into the scientific research sector, gaining invaluable insights and skills that will shape your academic, professional, and personal growth.   

Our science makes a real difference, enabling people and the environment to prosper, and enriching society. We are the custodians of a wealth of environmental data, collected by UKCEH and its predecessors over the course of more than 60 years.    

If you are passionate about environmental science and global challenges, our internship scheme is the perfect opportunity to gain hands-on experience and collaborate with excellent scientists.   

Key information   

• Fixed internship period: Monday 30 June to Friday 8 August 2025.   

• Our internships are offered at our Bangor, Edinburgh, Lancaster, and Wallingford sites, as specified below.  

• You will receive c. £2,847 (before deductions) in total for the 6-week programme.   

• Closing date for applications: Wednesday, 2 April 2025 we are expecting a lot of interest, so we recommend you apply early!   

• We expect to hold online interviews between Wednesday 30 April – Wednesday 14 May.  

Eligibility 

Interns must be excellent communicators, numerate and passionate about environmental science. Each internship has its own specific eligibility requirements (see internship descriptions below) but in general all applicants must: 

• Have the right to live and work in the UK for the duration of the internship. 

• Be able to work 37 hours per week, for the duration of the 6-week internship at the location of your chosen internship.  

• Be an undergraduate or postgraduate (Master or PhD) student at university or have graduated within the six months prior to the start of the internship (30 June 2025) 

• Be comfortable working independently with strong communication and interpersonal skills. 

• Act as a strong team player, comfortable both giving and receiving feedback openly. 

Successful applicants must also agree to the following: 

• Provide a one-page report to their supervisor describing the visit and its accomplishments, plus a short testimonial within 30 days of completion of the visit. 

• Agree to produce and present a poster or give a presentation at a UKCEH event. 

• Agree to appear in publicity and promotion materials for UKCEH. 

• Acknowledge the support of UKCEH National Capability for Global Challenges in any publications or presentations arising from the internship. 

You will carry out work for UKCEH under a contract of services agreement via Hays PLC (Company Number 02150950). You will be paid weekly by Hays PLC by submitting a timesheet to your UKCEH Line Manager. 

Please note: Unfortunately, we are unable to offer visa sponsorship for this role and this does not qualify for endorsement to support a Global Talent Visa application. 

The Internships  

Remember to specify in your cover letter which internship(s) you are applying for.  

Location: UKCEH Bangor, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd, LL57 2UW

INTERNSHIP 1: INVESTIGATING THE IMPACT OF OZONE POLLUTION ON BIODIVERSITY IN FOREST ECOSYSTEMS

Supervisors: Katrina Sharps (UKCEH), Felicity Hayes (UKCEH)

Project description: 

ICP Vegetation is an international research programme under the umbrella of the United Nations Economic Commission for Europe (UNECE), investigating the impacts of air pollutants on vegetation. Ozone is the most important phytotoxic air pollutant, negatively affecting photosynthesis and plant growth. 

The intern will conduct a review of the impact of ground-level ozone pollution on biodiversity in forest ecosystems, working as part of the ICP Vegetation Coordination Centre team at UKCEH Bangor.

 
The review will include an investigation of the potential impact of ozone on insects (including plant pollinator interactions) and how the pollutant may impact species richness, ecosystem structure, and forest dynamics. The intern will also produce a global map of modelled ozone levels alongside forest species richness, to highlight hot spot areas at particular risk from ozone pollution.

Tasks:

The key tasks of the intern will include planning the structure of the review, including key topics to focus on; searching for relevant literature and extracting useful information; writing the review; collating/processing relevant spatial data, including modelled ozone data, species richness index for forests and producing maps (using either GIS software or R/Python, depending on the expertise of the intern) revealing high risk areas. The intern will also produce a report and short presentation summarising the key results of the project.

Expected outcomes:

The main outcome of the internship will be increased understanding of how ozone pollution may impact biodiversity in forests, and how this may vary around the world. Key knowledge gaps and topics of concern will be identified that could be followed up by further work (and funding proposals) involving UKCEH international partners.

A report will be produced by the intern, which will be summarised as a policy document for presentation to the UNECE, with the aim of also publishing the results in a high impact scientific journal. 

The results will be part of the output for the year for ICP Vegetation, and they will be reported/shared at international meetings. This will complement existing work by UKCEH investigating impacts of ozone on carbon sequestration in forests. There is also the potential (time/dates permitting) that the results could be presented at a relevant international conference (e.g. IUFRO 2025), thus sharing the outputs of the project to a wide international audience.

Any data collated (licences permitting) could also be shared on the EIDC (Environmental Information Data Centre), for use in future projects by other scientists around the world.

In addition to having outputs of the internship shared internationally, the intern will benefit from gaining research experience working within a team of scientists who are established experts in their field and gaining directly transferrable skills for any future employment or further post-graduate level studies.

Required skills and background:

Essential: 

• Currently in undergraduate or postgraduate (not including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025)
• Background in environmental science/ecology
• Good skills in report writing and reviewing scientific literature
• Experience with GIS software and/or R, Python
• Good time management and strong communication skills

Location: UKCEH Edinburgh, Bush Estate, Penicuik, Midlothian, EH26 0QB

INTERNSHIP 2: TOWARDS A UNIFIED APPROACH TO ENVIRONMENTAL MODELLING

Supervisors: Joe Marsh Rossney (UKCEH), Ed Rowe (UKCEH)

Project description:

Models are an essential tool for understanding and predicting environmental change. There are usually several different approaches to modelling the same physical processes, each with their own strengths and weaknesses. For example, while essentially everyone agrees on the fundamental equations that describe atmospheric dynamics, there are many weather forecasting models that make different technical choices and produce different predictions.

Although diversity and competition among models is good for science in principle, the reality is that issues of a more practical nature prevent us from fully benefiting from this multiplicity of models. The problem is that different models are generally hard to compare (due to e.g. different inputs and outputs, spatio-temporal scales), hard to combine (e.g. due to differences in design, the choice of programming language and use of software libraries), and hard to learn (even for experienced researchers!).

This situation has led to increasing interest in more structured frameworks for model development that make models more comparable and interoperable by design. A good example of this is the so-called Basic Model Interface (BMI). In simple terms, the BMI enables models with different internal mechanisms to be operated through the same set of Python commands, allowing models to interface with analysis scripts and other models in a consistent way.

Several research-grade models have already been reformulated to adopt the BMI, but its potential as an educational scaffold has been under-explored. We (the supervisors) have proposed to lead the development of a collection of pedagogical numerical models that are operated through the BMI, accompanied by instructional resources such as notebooks and interactive dashboards.

The purpose of this internship is to contribute to this effort, primarily by developing models that adhere to the BMI, and by creating tutorial notebooks that use these models to demonstrate good scientific and software practices. The code and tutorials will be open-sourced and freely licensed for use or adaptation.

This internship would suit a student who is interested in the internal mechanics of numerical models (i.e. the equations and code) and the unifying principles of modelling. The student will be expected to take ownership of their development work, solve technical problems, discuss ideas and findings with the supervisors, and disseminate their outputs using appropriate channels.

Tasks:

The first 2-3 weeks will be dedicated to a warm-up task. The student will implement the Basic Model Interface for a simple model written in Python and create instructional material in the form of executable notebooks (e.g. Jupyter, Rmarkdown, Pluto.jl or Quarto). In doing so they will gain familiarity with the BMI framework and core tools of modern scientific software.

At the half-way point the student will be in a good position to decide on the direction they would like to take in the second half of their internship. A student looking to improve their software development skills might choose to develop their own BMI-compliant model, chosen to match their scientific or other technical interests. A student interested in education and community building would be encouraged to develop additional learning resources such as interactive dashboards using BMI models. A third, more research-oriented option would be to investigate the possibility of implementing the BMI for one of the 
flagship scientific models developed by UKCEH scientists.

Expected outcomes:

1. At least one fully developed and documented model implementing the BMI, accompanied by a collection of tutorial notebooks, published online (e.g. using GitHub) and freely available for anyone to use and extend.
2. The student will be expected to join and engage with the CSDMS community discussion forum, a resource which they will be able to draw on during and after the internship.
3. The student’s contributions will be credited on GitHub, the CSDMS Community Model Repository, and any other forums where the model appears, allowing them to highlight their achievements on their CV. 
4. The student should produce a technical report of their work, and a short blog post reflecting on their experiences during the internship.

Required skills and background:

Essential: 

• Currently in undergraduate or postgraduate (including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025)
• Intermediate level programming skills, including some experience with Python 
• Some familiarity with the principles of modelling and the underlying mathematics; preferably the student has taken a course in numerical methods or computer modelling. However, the student need not have experience with environmental models specifically. 
• An enthusiasm for learning and, importantly, sharing knowledge with others, e.g. through teaching, participation in learning communities, or contributing to open-source software. (Note enthusiasm does not require previous experience).

Desirable: 

• Experience with git and GitHub, either via the command line, through editor integrations or GitHub desktop.
• Some experience with Linux and knowledge of basic shell commands (to change directory, copy files etc).
• Experience with any of R, Julia, C, C++ or FORTRAN. Familiarity with a compiled language would be helpful for a conceptual understanding of research models

INTERNSHIP 3: RESTORING FOR ECOLOGICAL RECOVERY AND RESILIENCE: A GLOBAL MODELLING APPROACH

Supervisors: Klementyna Gawecka (UKCEH), James Bullock (UKCEH)

Project description:

Ecosystem restoration is widely recognised as a critical solution to addressing the global biodiversity loss and climate crises. However, to ensure their sustainability, restoration actions must be designed to achieve effective recovery and long-term resilience of the restored ecosystems. This requires predictive models capable of capturing the dynamics of complex ecological systems, accounting for the interactions among species and their relationship with the landscape. However, the lack of a general, globally applicable modelling framework currently limits the effectiveness of our restoration efforts.

This project will contribute to advancing our understanding of the ecological consequences of landscape restoration. The internship will focus on landscape configuration – examining global trends and its influence on recovery and resilience of ecological communities. This will involve the analysis of empirical landscape data from around the world, as well as computational modelling. The intern will conduct research under the supervision of scientists from UKCEH and in collaboration with researchers from the University of Zurich and ETH Zurich in Switzerland.

This internship provides a unique opportunity to:

• Learn about ecological modelling and network analysis approaches, and their application to ecological conservation and restoration.
• Engage with a multidisciplinary research team, including leading experts in ecology and restoration science.
• Use programming languages such as R and Julia for modelling and data analysis.
• Develop key skills in data synthesis and analysis, coding, scientific writing, and collaborative teamwork.
• Contribute to strategies for effective and resilient ecosystem restoration, supporting global efforts to combat biodiversity loss and climate change.

Tasks:

• Reviewing and synthesising published research on landscape configuration globally
• Parameterising community dynamics models using data obtained from published literature
• Performing computational simulations and analysing outcomes
• Collaborating with project partners
• Reporting and presenting findings

Expected outcomes:

• A written report summarizing project's findings, with the potential for submission as a journal publication.
• Development of intern’s quantitative and communication skills.
• Deeper understanding of ecological restoration, specifically the relationship between landscape configuration and ecosystem resilience at the global scale.
• Generation of practical recommendations on the design and management of restoration projects.

Required skills and background:

Essential:

• Currently in undergraduate or postgraduate (including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025)
• Background in ecology, zoology, environmental or physical science
• At least an intermediate level of coding proficiency in programming languages such as R or Julia
• Interest in conservation or restoration
• Ability and enthusiasm to learn and apply quantitative skills

Desirable:

• Knowledge of network analysis
• Experience in population dynamics models
• Interest in a career in scientific research or conservation practice

INTERNSHIP 4: USING HIGH RESOLUTION SATELLITE IMAGERY TO DETECT CANOPY PHENOLOGY EVENTS IN TROPICAL FORESTS IN SOUTHEAST ASIA

Supervisors: Beth Raine (UKCEH), Lindsay F. Banin (UKCEH)

Project description:

Seasonal and interannual periods of low rainfall (such as the El Niño drought events) strongly affect the ecology of trees in Southeast Asian forests. Dry seasons can trigger leaf fall and leaf flushing cycles and can also initiate synchronised flowering and fruiting events. However, we lack a comprehensive understanding of these key ecosystem processes, and therefore how future changes in frequency and intensity of drought events may impact forest function. The recent availability of higher spatial and temporal resolution satellite data offers the opportunity to detect these events and relate the timing and intensity of them to other interacting environmental change factors, especially forest fragmentation and edge effects.

We invite an intern to co-develop relevant hypotheses, develop and carry out an analysis to test the detection of canopy phenology events in tropical forests at selected sites in SE Asia from satellite imagery. The intern would use open-source high-resolution Planet imagery to develop a workflow to identify flowering, fruiting and foliar change in areas with known historic canopy phenology events, including potential sites in Malaysia and Indonesia. Depending on the outcome of this core piece of work and the intern’s interests, they will then extend the work by applying the methods developed to specific questions which may include exploring spatio-temporal patterns in reproductive phenology and relationships between phenology signals and proximity to forest disturbance.

The intern would be supervised in Edinburgh but with additional opportunities to interact with Earth Observation researchers at other sites through virtual meetings.

Tasks:

Core work (weeks 1 - 4):

• Familiarise with the literature on canopy phenology in forests and monitoring these events with remote sensing imagery and hone focus of the internship with supervisors
• Familiarise with accessing and downloading satellite imagery available on Planet labs
• Download and pre-process time series of Planet satellite imagery for a focal landscape that has undergone known historic phenological or drought events (for example, this may include the large-scale flowering in Malaysian tropical forests in April – May 2019)
• Extract features from satellite imagery time series and compare these during and before/after phenological events
• Identify differences in satellite imagery features during and before/after phenological events using e.g. general linear model or random forest approach
• Develop a collaborative, reproducible workflow using version control Continuation (weeks 5 – 6): Depending on the outcomes of the core section of the work, the intern could then use the models developed to answer questions about phenological events, their detectability and the wider landscape context. Possible areas include:
• Explore how canopy events occur over space and time and in association with other landscape features
• Explore phenological events in another landscape • Compare detectability of phenological events with other satellite imagery

Expected outcomes:

• Reproducible code to process and analyse satellite imagery of phenological events in selected sites in SE Asia (e.g. R scripts, a GitHub repository of code and/or Rmarkdown document)
• A report detailing the methodology and results from the analysis, including appropriate figures
• Analysis suitable for inclusion in a peer-reviewed publication • Intern develops their knowledge of forest ecology and applied skills in GIS, remote sensing and data analysis

Required skills and background:

Essential:

• Currently in postgraduate education (Masters or PhD) at university or have graduated within the six months prior to the start of the internship (30 June 2025) - studying ecology, biology, geography or a related quantitative field
• Experience using GIS
• Experience coding in R
• Good quantitative skills

Desirable:

• Experience using GitHub for version control
• Experience using satellite imagery, especially PlanetLabs data

Location: UKCEH Lancaster, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, LA1 4AP

INTERNSHIP 5: INVESTIGATING THE NUTRIENT RETENTION DYNAMICS OF BIOCHAR, IMPLICATIONS FOR MALAYSIAN OIL PALM AGRONOMY

Supervisors: Ashley Taylor (UKCEH), Samuel Robinson (UKCEH)

Project description:

We seek to further our understanding of biochar nutrient dynamics to better determine the viability of biochar as an agricultural amendment, with potential impacts on soil health, crop productivity, and greenhouse gas emissions. The intern will be primarily involved in laboratory-based work, contributing to active research, helping to develop methods/experiments and build capacity for future biochar research within the plant soil interactions (PSI) group. These experiments will feed into ongoing international work investigating the ability of biochar to improve soils health under oil palm agriculture, a rapidly growing land use and cause of extensive soil degradation across Southeast Asia.

Tasks:

1. Research, plan and test lab-based methods for measuring adsorption and desorption of nutrients from biochar samples. This is building on existing work within the group, which will serve as the pilot for the work of this internship.
2. Carry out an experiment based on above-mentioned methods to determine the adsorption/desorption dynamics of Malaysian oil palm-derived biochar.
3. Conduct a desk-based literature review into the relevance of adsorption isotherms to the work of the PSI group.
4. (Subject to experiment timing) Contribute to lab-based soil incubations investigating potential nitrous oxide suppression by biochar and enhanced rock weathering to Malaysian oil palm soils.

Expected outcomes:

1. The primary outcome of the internship is to provide the intern with experience of working in a functional and field leading environmental research lab. This will range from basic lab skills through to operating analytical instrumentation and helping in the design of experiments.
2. The intern will gain some knowledge of the greenhouse gas removal field of research as well as soil biogeochemistry more broadly.
3. Multiple standard operating procedures (SOP’s) detailing a rigorous, repeatable, and feasible method for understanding the nutrient dynamics of biochar.
4. Contribution to novel and field-leading greenhouse gas removal research.

Required skills and background:

Essential:

• Currently in undergraduate or postgraduate (not including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025)
• Basic understanding of good lab practice, including safe operation and quality assurance.
• Basic understanding of soil biogeochemistry, knowledge of nature-based greenhouse gas removal technologies desirable.
• Ability to understand and follow a standard operating procedure (SOP) to achieve robust and repeatable results in lab-based experiments.
• Ability to take the initiative and make well-reasoned decisions independently, while following guidelines/instruction of supervisors.
• Keen to learn new skills and get involved in practical research.

Location: UKCEH Wallingford, Maclean Building, Crowmarsh Gifford, Wallingford, OX10 8BB

INTERNSHIP 6: IDENTIFYING DRIVERS OF INCREASED THUNDERSTORM FREQUENCY OVER INDIA

Supervisors: Emma Barton (UKCEH), Jawairia Ahmad (UKCEH), Christopher Taylor (UKCEH)

Project description:

Across India, large thunderstorm clusters called Mesoscale Convective Systems (MCSs) contribute up to 70% of the total rainfall during the Indian Summer Monsoon (ISM). MCSs are also associated with destructive hazards such as high winds, intense rainfall and lightning which can result in loss of life and infrastructure.

A 20-year global MCS dataset suggests a trend of increasing storm frequency during the ISM over a region where irrigation has been expanding over a similar timeframe. The role of the intern will be to identify and analyse significant trends in atmospheric and surface properties to characterise the processes driving the increase in storm frequency. In particular, the intern will evaluate whether human activity (i.e. irrigation expansion) is contributing to the observed trend.

Tasks:

• Compute trends in surface and atmospheric properties (e.g. surface fluxes, temperature and humidity) using satellite and reanalysis products.
• Use statistical methods to isolate significant trends.
• Analyse temporal and spatial correlations between significant trends to identify the physical processes driving the storm trend.

Expected outcomes:

The project will determine whether the observed storm trend can be attributed to irrigation expansion. This could be crucial information for agricultural planning, particularly with the increasing demand for food with population growth. The findings could have implications for other regions of the world where MCSs are a threat to the population and irrigation has been expanding, including China and North America.

Results from the analysis will contribute to an academic paper, including figures produced by the intern, such as spatial maps of significant trends. The project tasks will provide the intern with experience in statistical and geospatial data analysis, producing publication quality outputs and working with a diverse team of scientists at UKCEH.

Required skills and background:

Essential:

• Currently in undergraduate or postgraduate (not including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025)
• Interest in extreme weather and atmospheric dynamics
• Ability with a scientific programming language (preferably Python)
• Very good numeracy / statistical ability

Desirable:

• Experience with remote-sensing or reanalysis datasets
• Background in meteorology

INTERNSHIP 7: TRAINING NEEDS ASSESSMENT AND FUNDING SOURCES FOR INTERNATIONAL AUDIENCES

Supervisors: Ingo Schüder (UKCEH), Yasmin Matthews (UKCEH), Nick Wells (UKCEH)

Project description:

You will work with the UKCEH Commercial Training team to explore possible options to increase the international reach of the UKCEH training offer. The Commercial Training team delivers solution-focused training activities for academia, government organisations and businesses on a broad range of environmental science topics.

The scope of the project includes:

1. Doing market research and gap analysis for training provision
2. Identifying potential funding sources
3. Training topics relevant to UKCEH (for our current portfolio of training topics see https://www.ceh.ac.uk/solutions/training)
4. Countries in receipt of Overseas Development Assistance (ODA)

Tasks:

There can be some flexibility in the tasks you undertake, depending on your personal background, skills and experience. The tasks are comprised mostly of desk-based review and data collation, with some potential for using survey techniques to gather information from organisations and individuals. The tasks include:

1. Gap analysis, to gain a better understanding of what other training is available and how this is operated, as well as assessing research training needs within our target audiences, and scoping out potential training delivery partners in ODA countries.
2. Market research on how best to promote and enable uptake of our training offer, including understanding the best channels through which to engage, improving the accessibility of our training offer in terms of preferred training methods (online vs. In person) in our target countries, as well as the willingness and ability to pay, and assessing training priority needs.
3. Investigating funding opportunities within the UK and internationally, that support training through provision of individual sponsorships/grants, or 3rd party funding to make training more accessible. These may include development aid organisations, foundations, philanthropy, government grants, and so on.
4. Scoping course development to respond to the needs of target audiences, exploring options for translating existing courses to make them more accessible, for example.

Expected outcomes:

Through this internship, you will contribute to UKCEH being better poised to meet international environmental science training needs more inclusively by understanding the barriers and possible mitigations to accessing training, as well as the specific training needs. This in turn will contribute to enabling a more diverse environmental science community who can embed science into decision-making around global environmental challenges.

Through the internship, you will contribute to the development of two databases – one on other training providers and the other on potential funding sources. You will also develop a shortlist of possible partners with whom UKCEH can deliver training and identify the best routes for promoting training offers to the target audiences.

By working with UKCEH, you will have the opportunity to learn more about the training needs and provision within an ODA context and contribute to making knowledge sharing more equitable. You will have the opportunity to exercise your research and data collation skills, as well as build networking and communication skills. You will have the opportunity to learn from an advisor with extensive experience in lifelong learning and training development. You will also get to work in a scientific organisation with a diverse and highly skilled staff in various scientific and professional disciplines.

Required skills and background:

Essential:

• Be an undergraduate or postgraduate (Master or PhD) student at university or have graduated within the six months prior to the start of the internship (30 June 2025) in Environmental Science or International Development or Lifelong Education (or similar)
• Interest in lifelong learning, training, knowledge sharing and capacity building
• Good online search skills, having a good search strategy and method of capturing information in an organised way
• Being well-organised
• Good team player
• Being able to work independently

Desirable:

• Knowledge of the ODA context
• Experience of working in an ODA country or with an organisation from an ODA country
• Other languages of target countries (e.g. Spanish, French, Mandarin)
• Any previous marketing experience
• Technical knowledge of online learning platforms
• Understanding of topics relevant to the UKCEH training offer

INTERNSHIP 8: DERIVING FUNCTIONAL PLANKTON METRICS FROM IMAGING FLOW CYTOMETRY: TOWARDS A FAIR APPROACH

Supervisors: Isabelle Fournier (UKCEH), Mike Bowes (UKCEH), Emilie Poisson Caillault (Université du Littoral Côte d'opale)

Project description:

Flow cytometry is a technique that groups particles based on their interaction with light (scattering and fluorescence), often through fixed wavelength lasers. The molecules that allow phytoplankton to conduct photosynthesis, the photosynthetic pigments, naturally interact with light, producing fluorescence. As different groups of phytoplankton contain different pigments in varying ratios, the signal acquires by flow cytometry can be analysed to quantify the abundance of phytoplankton groups.

Flow cytometry data analysis is currently undertaken by experts through flow cytometry software developed by the instrument companies. This leads to variability in settings, the grouping, the name given to them and the abundance of cells in each group, depending on the instrument\software and the expert that created the analysis protocol. The variability makes it difficult to compare data from different laboratories and between different instruments, and limit the use of this technique in large scale studies. An increasing number of agencies and scientists are developing frameworks to normalise the analysis of phytoplankton through flow cytometry, moving towards a FAIR approach (Findable, Accessible, Interoperable, Re-usable). In the phytoplankton field of research, it can be achieved by gaining consensus on and applying open-source classification on raw data.

Flow cytometry data is a rich source of information on phytoplankton, particularly when the technique is coupled with imagery. Imaging flow cytometry allows insight into natural phytoplanktonic communities at unprecedented levels, because it produces a coupled image\fluorescence dataset quicker and cheaper than any traditional approaches. One of the key gaps that imaging flow cytometry can fill is the ability to quantify non-photosynthetic plankton.

UKCEH has developed an expertise on imaging flow cytometry and has used it to increase the understanding of river ecology and the causes of phytoplankton blooms. This project aims to develop or adapt an open-source image clustering algorithm and an open-source classification workflow of fluorescence data to automate the quantification of the functional plankton metrics derived from imaging flow cytometry. This work will involve metrics such as the classic photosynthetic plankton abundance and the novel non-photosynthetic plankton abundance.

Tasks:

• Manual counting of non-photosynthetic plankton through flow cytometry images.
• Develop/adapt and apply an open-source image clustering algorithm to automate the non photosynthetic plankton counting.
• Develop/adapt and apply an open-source classification workflow to automate the analysis of flow cytometry data for photosynthetic plankton.

Expected outcomes:

• A workflow specifying the required steps towards an automated analysis of flow cytometry data for photosynthetic plankton from the Attune Cytpix.
• Extracted data from the Attune Cytpix, formatted and ready to be tested. Tested if possible.
• An image clustering algorithm to automate the non-photosynthetic plankton counting from the Attune Cytpix images, on GitHub.
• Given the novelty of the approach and the data that it is going to generate, this project can lead the intern to be part of multiple scientific papers.

Required skills and background:

Essential:

• Currently in postgraduate (including PhD) education at university or have graduated within the six months prior to the start of the internship (30 June 2025) - either informatics/data science with an interest in aquatic ecosystems OR ecology/biology with a working knowledge of machine learning
• Experience with machine learning (class imbalance, parameters selection and tuning, etc.)
• Coding with Python or R

Desirable:

• Familiarity with flow cytometry, community ecology statistics, or freshwater ecology.

We need three things from you:    

1. A letter of support from your supervisor, tutor, director of studies, or equivalent, confirming their approval for you to undertake the proposed summer internship    
2. Upload a covering letter outlining your motivation for joining the internship scheme and specifying the internship you're interested in, along with reasons for your choice    
3. A brief CV detailing your educational background, professional experience, and any publications you may have    

   Before you submit your application through our careers page, please ensure that: 

• You meet the general eligibility requirements for the internship scheme.  
• You specified in your cover letter which internship you are applying for. 
• You have thoroughly reviewed the internship description, paying close attention to the specific requirements for the internship. 
• You are able to submit all three documents outlined above. Note incomplete applications will not be considered.