NEWS 2021-02-21

Data Scientist – AI, People and Planet

Do you want to develop research applying artificial intelligence for sustainability, and help build a capacity at the Beijer Institute and our collaborators to apply such methods to help tackle urgent sustainability challenges?

Climate change and our rapidly changing planet is fundamentally shaped by technological change. At the same time, there seems to be a growing yet poorly explored potential for methods and approaches that build on artificial intelligence (including machine learning and deep learning) to bring out novel and important insights from social, economic and ecological data of use for society, and for the sustainability sciences. We are looking for a Data Scientist to help us and our international partners discover, explore and apply AI-methods to help tackle important sustainability challenges.

This position is announced in collaboration with the Urban Systems Lab at the New School (New York, USA) as part of a growing network of institutions under the international initiative “AI, People and Planet” (more information below).

The Beijer Institute of Ecological Economics at the Royal Swedish Academy of Sciences and its programme “Governance, Technology and Complexity” and its international initiative “AI, People and Planet” in collaboration with the Urban Systems Lab (New School) and Princeton University is advertising a 1-year position, which includes two types of work tasks, each of them of equal weight (50% each).

The first relates to the candidate’s own research that helps advance applications of AI-methods to tackle important sustainability challenges, with a priority for those challenges that relate to our living planet, the world’s oceans, and cities. The second relates to collaborative work together with colleagues at the Beijer Institute of Ecological Economics, the Stockholm Resilience Centre (Stockholm University), and the Urban Systems Lab (New School, New York) to help advance applications of AI-methods (including machine learning, and deep learning when possible) for sustainability science and action.

Apply before 7 April.

For more information and how to apply:

Click here