Senior Research Associate - Earth Observation Data Scientist

University of Bristol
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lecturer/Senior Lecturer/Associate Professor in Artificial Intelligence

Lecturer/Senior Lecturer Data Science

Applied AI and Machine Learning Scientist - Senior Associate

Call for Associate Editor Applications: Computation, AI and Machine Learning

Head of DevOps and DataOps

Machine Learning Engineer (Manager)

The role

Based in a world leading research team composed of members of the Visual Information Lab (VI-Lab) within the School of Computer Science at the University of Bristol, this is an exciting opportunity to join a strong group developing state of the art synthetic aperture radar (SAR) image processing and analysis technologies. You will be immersed within the AssenSAR team, which has over the last seven years designed AI systems for the detection and identification of ships in satellite SAR imagery in order to enable maritime traffic monitoring. The work will focus on the Celtic Sea, one of the most important areas for offshore wind in the UK.


What will you be doing?

You will be responsible for conducting cutting edge research in remote sensing image processing and AI to develop state-of-the-art SAR imaging algorithms. These will serve the purpose of mapping fishing and leisure vessels in the Celtic Sea and investigate their impact to offshore wind. You will collaborate with The Crown Estate and Celtic Sea Power, the two industrial project partners, and help improve their understanding of vessel density and location, which will be incredibly valuable for their planning of marine spatial and habitat restoration efforts.


You should apply if

The successful applicant will have:

A PhD in Computer Science, Electrical and Electronic Engineering, or Mathematics. Proven experience of signal/image processing and analysis techniques. Experience with Earth Observation systems Experience of programming using Python and Matlab and/or C++

Strong mathematical background.


Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.