Research Assistant / Research Associate in three dimensional forest ecology (Fixed Term)

University of Cambridge; Cambridge; Hybrid; GBP 42,000 - 54,000; Full time

Job summary

University of Cambridge invites applications for a Research Associate to work on a UKRI Future Leaders Fellowship project focused on next‑generation forest dynamics modelling using remote sensing data. The postdoc will analyse three‑dimensional forest datasets from multiple European forests and contribute to new insights into how forest structure relates to tree diversity across biomes.

The role may involve some fieldwork in the UK/Europe and will center on statistical and computational analysis

Qualifications

• PhD in ecology, environmental science, remote sensing, or related field, by start date.
• Experience with LiDAR or high-resolution remote sensing for ecological applications.
• Excellent quantitative and statistical skills.
• Strong written and verbal communication; able to work collaboratively.
• Ability to produce high-quality peer‑reviewed outputs appropriate to career stage.
• Willingness to undertake short fieldwork in the UK/Europe.
• Hybrid working possible for exceptional Cambridge candidates.

Responsibilities

• Analyse existing datasets to advance forest dynamics modelling.
• Lead statistical and computational analyses; contribute to research outputs.
• Collaborate with international partners; develop study topics (allometry or morphology) per interests.
• Assist with short periods of fieldwork in forests in the UK or Europe.
• Prepare results for peer‑reviewed publications and project reports.

Skills

LiDAR data

Remote sensing

Quantitative analysis

Open science

Written communication

Collaborative work

Peer‑reviewed outputs

Computational researcher

Education

PhD in ecology, environmental science or remote sensing

Tools

LiDAR software

R

Python

Job description

Applications are invited for a Research Associate to join Dr Emily Lines' UKRI Future Leaders Fellowship project "Next generation forest dynamics modelling using remote sensing data" funded by UKRI.

This postdoctoral position will make use of three-dimensional datasets already collected and processed from diverse forests in five European countries using terrestrial and drone laser scanning, drone photogrammetry and orthomosaic imagery. The role will leverage this unique dataset to generate new understanding about how the relationship between forest structure and tree diversity varies across biomes.

The successful candidate may also have the opportunity to expand dataset coverage by collaborating with our international partners. Depending on the interests of the successful candidate, the role will focus on either 1) individual allometry, exploring how allometric relationships for tree structural metrics, biomass and leaf area vary by species and biome, or 2) how tree and forest morphology, structural complexity and tree structural plasticity are controlled by stand species composition and competition in contrasting abiotic conditions. Candidates should express their initial preferences within their cover letter, briefly explaining the reasons for their interest and how their previous experiences would inform their approach.

Although we anticipate the role will focus on analysing existing data, we anticipate that the postholder will contribute to short periods of fieldwork in forests in the UK or Europe.

The successful candidate must have, or be about to finalise, a PhD in ecology, environmental science, remote sensing, or other relevant discipline, and this must be submitted by the start of the appointment.

Salary Ranges: Appointment at Research Associate level (Grade 7) is dependent on having a PhD. Those without a PhD will be appointed at Research Assistant level (Grade 5). Those who have submitted but not yet received their PhD will initially be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

They should have experience working with LiDAR data or other high resolution remote sensing for ecological applications. They must have excellent quantitative and statistical skills, and be a confident computational researcher with a demonstrated commitment to open science standards. They should have excellent written and verbal communication skills, and evidence of successful collaborative work. They must show evidence of the ability to produce and lead high quality collaborative peer-reviewed research outputs commensurate with career stage; we value quality of outputs over quantity in this respect.

The role will primarily focus on statistical and computational analysis of existing datasets. However, contribution to short periods of field data collection will be expected so field experience is a positive. Candidates will be supported in training needs and will have access to generous project funds for workshops, conferences and travel. They must be highly motivated and should have excellent written, organisational and communication skills, and be able to work well as part of a team.

The successful candidate will be based in Cambridge. Hybrid working patterns may be considered for exceptional candidates, although some aspects of the work may require periods of continuous in-person attendance.

Please refer to the Further Particulars attached for more comprehensive information on the qualifications, skills required and role duties.

Fixed-term: The funds for this post are available until 31 October 2027 in the first instance.

Questions about the role should be directed to Dr Emily Lines, [e-mail]. If you have questions on the application process, please email Mrs Alessandra Uomo, HR Coordinator, at [e-mail].

Interviews are anticipated to take place in Cambridge or remotely the week commencing 14 September 2026, or soon after.

Please quote reference LC50715 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.