FDL Research Sprint

Frontier Development Lab

Internship 3 months remote, UK

Uploaded 20 Dec 2021

Job Description

Interdisciplinary team-work is at the heart of the FDL philosophy, and our experience with previous participants has shown that the mix of skillsets creates results far bigger than the sum of the individual parts. Working closely together during the intense eight-week research sprints, teams have achieved outputs that might otherwise have taken years.

Our international network is growing, and provides great value for its members, many of whom have continued work on projects they began at FDL. 

Researchers have gone on to join some of the world’s most prestigious tech, earth observation companies and academic institutions in a variety or roles. FDL projects have been featured in peer-reviewed journals, conference presentations, posters and numerous news features.

What happens during the research sprint? 

Bootcamp week (week 1) aims to introduce AI methods and the basics to the space science team members and vice versa.  This is a rapid learning week where the core aspects of both fields are covered, ensuring everyone has a firm basis for the following weeks.

The next step is working in teams of four to get hands-on researching the challenge area and formulating solutions for the challenge question. The second half of the sprint is used for prototyping, testing and perfecting your solution, and getting ready to present it.  

At the end of the research sprint, teams will present their work to leading scientists, researchers and industry experts from NASA, ESA and our Partners.   

Person Specification

Over the four years FDL has been running we have seen a shift as more applicants bring both ML and space science experience, we call these people hybrids. Nonetheless, hybrids normally have clear ‘lead’ knowledge and experience in ML/data science OR space science. We are asking all applicants to nominate themselves as one of four categories:

ML/AI/ Data Scientist

Space Scientist

Hybrid - Space Science Lead

Hybrid - ML Lead

It will not prejudice your application in any way whichever you choose. A frank self-assessment of your skills is more important.