PhD in Deep Learning for Predicting Poverty from Satellite Images

The Data Science and AI division at the Department of Computer Science and Engineering is recruiting a PhD student for a project in Observatory of Poverty, within the research project Observatory of Poverty, funded by the Swedish Research Council (SRC).

Information about the research and the project
About 900 million people — one-third in Africa — live in extreme poverty. Operating on the assumption that life in impoverished communities is fundamentally so different that it can trap people in cycles of deprivation (‘poverty traps’), major development agencies have deployed a stream of development projects to break these cycles (‘poverty targeting’). However, scholars are currently unable to answer questions such as in what capacity do poverty traps exist; to what extent do these interventions release communities from such traps — as they are held back by a data challenges.

There is a lack of geo-temporal poverty data; this project will develop new methods to produce such data. Consequently, the aim of this project is to identify to what extent African communities are trapped in poverty and explain how competing development interventions alter these communities’ prospects to free themselves from deprivation. To achieve this aim, the project will tackle the following objectives:

Obj1: To train learning algorithms to identify poverty from satellite images, of African communities over time and space, quarterly, from 1984 to 2020.
Obj2: To examine how World Bank (WB) development programs versus Chinese programs, select African communities, and how these affect communities’ chances of breaking the cycle of deprivation (using the data of Obj1).
Obj3: To develop theories of the varieties of poverty traps by examining the extent to which these traps lurk in different social contexts that shape both local governance and public-service provisioning, and how these contexts may be more or less important for Chinese- or WB-styled projects.
Obj4: To create a statistical package—PovertyMachine—that enables us, and other scholars, to produce poverty estimates (Obj1) and conduct comparative program evaluations (Obj2 and Obj3).

The doctoral student is expected to pursue research mainly related to the project’s first and fourth objectives.

The project is a collaboration between Chalmers University of Technology, the Department of Sociology, University of Gothenburg, and the Institute for Analytical Sociology, Linköping University. Accordingly, although the student will pursue a PhD in machine learning within computer science and engineering at Chalmers, this person expected to have an interest in social-scientific issues and interdisciplinary research.

Major responsibilities
Your main responsibility as a PhD student is to pursue your doctoral studies within the framework of the outlined research project. You will be enrolled in a graduate program in the Department of Computer Science and Engineering. You are expected to develop your own ideas and communicate scientific results orally as well as in written form. In addition, the position includes 20% departmental work, mostly as a teaching assistant in Chalmers' undergraduate and masters-level courses or performing other departmental tasks.

Position summary
Full-time temporary employment. The position is limited to a maximum of five years.

Qualifications
To qualify as a PhD student, you must have a master's-level degree, or a four-year bachelor's degree, corresponding to at least 240 higher education credits in computer science, mathematics, signal processing, physics or related field at the time of start and experience in machine learning. The position requires sound verbal and written communication skills in English. Knowledge of Swedish is not a prerequisite for applying since English is our working language.

Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.

Our offer to you
Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees. Chalmers offers Swedish courses.

Application deadline: 1st May, 2021

APPLY HERE

For questions, please contact:
Adel Daoud, CSE DSAI daoud@chalmers.se
Fredrik Johansson, CSE DSAI, fredrik.johansson@chalmers.se

*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***