Assistant Research Scientist - Autonomous Systems

Our Biological Carbon Cycling group investigates how marine ecosystems influence biogeochemical processes and the ocean carbon cycle, using autonomous platforms, field observations, laboratory measurements, and modelling. In this role, you will help convert raw observations from autonomous platforms into high quality, science ready datasets and apply quantitative methods to environmental time series.

You will play a key part in developing a standardised, open-source data processing pipeline for autonomous platforms such as underwater gliders and floats. Working closely with researchers, engineers, technicians, and data specialists, you will design and implement Python tools, improve data workflows, and help ensure that processed datasets are robust, well documented, and ready for scientific use across a wide range of oceanographic applications. You will also have opportunities to contribute to scientific analyses, data synthesis, and the preparation of research outputs.

The role offers an excellent opportunity for candidates from academia or industry who are motivated by technical problem solving, scientific enquiry, and collaborative development. You will build on your existing experience in quantitative environmental data analysis and modern scientific software practices while working with cutting-edge autonomous ocean observing technologies in a multidisciplinary research environment.

This role is offered as an open-ended Band 7 position. The post holder will have opportunities to develop their technical and scientific skills through close collaboration with experienced researchers and ongoing projects across NOC. They will be supported to continue building their expertise in autonomous observations, quantitative analysis, and scientific software development, and will have scope to contribute to collaborative research activities and to grow their professional profile within the wider community.

Do you have experience in Signal processing?, Do you have a Master's degree?, You will have, at a minimum, a MSc in a strongly quantitative discipline such as physics, mathematics, engineering, earth sciences, or computer science, or have equivalent research experience. You will have academic or industry experience in timeseries analysis and signal processing. You will bring strong Python programming skills, including practical use of object-oriented approaches, and you are comfortable working with observational or environmental datasets. Experience with version control systems such as Git is essential for collaborative development. You should be able to show a portfolio of code in a developer platform like Github, Gitlab.

You will have comprehensive quantitative and analytical skills, and while experience with the calibration or processing of sensor data, or with datasets from autonomous platforms (especially gliders and floats) is highly desirable, it is not required. You communicate clearly, work well in multidisciplinary teams, and approach technical and scientific challenges with curiosity and initiative.

This role is suited to someone who wants to develop further at the interface of scientific computing and oceanography, contributing to both tool development and scientific analysis.

Why NOC?

We offer a generous set of benefits, including:

• 30 days contractual annual leave, plus 3.5 extra closure days and bank holidays

• a 10% employer contribution pension scheme (with a 15% NI saving on employee contributions made above 5%, if in the salary sacrifice scheme)

• financial support for relocation

• access to our Employee Assistance Programme, offering free and anonymous support on mental, physical, emotional, health and financial issues

• access to a flexible benefits portal offering online discounts, cashback and eGift cards

• a Cycle2Work scheme allowing employees to acquire bikes and accessories

• a great working environment with a number of social events, including summer and Christmas celebrations

• we are proud to be a Living Wage Employer

We are the National Oceanography Centre (NOC) - the UK's leading institution for integrated coastal and deep ocean research. Through our ground-breaking research, collaboration, and game-changing innovation we work to gain a deeper understanding of our ocean, helping every living thing on our planet flourish. We are made up of a dynamic and vibrant community focused on solving challenging long-term marine science problems, underpinning international and UK public policy, business and societal outcomes. The ocean has the potential to provide the solutions to so many of the social, economic and environmental challenges we face worldwide. To truly harness the value of the ocean, we put ocean research, science and discovery at the heart of our culture. Join us in shaping the future of oceanographic research and contribute your unique perspective to our organisation., This position will be based in Southampton. The centre is well connected by public transport and has ample cycle parking in addition to free onsite car-parking with over 40 EV charging points., 1) Please describe a project where you applied time series analysis or signal processing methods to environmental or observational data. Briefly outline the dataset, the methods you used, and the key outcomes (300 words max). 2) Provide an example of how you have used version control and reproducible workflows in a scientific or technical project. Describe the tools you used and how these practices supported collaborative or reliable development (300 words max).