Research assistant for spatial analysis

We are searching for a research assistant to support spatial analysis in multiple projects, whose primary responsibilities will be in the form of data extraction, data preparation and classification, and code review.

The main project investigates frontier topics related to spatial economics that will be supported by data extraction from online sources, data classification and analysis, and implementation of numerical methods.

The successful candidate in the competition will execute the following tasks:

1) Extract information on key Points Of Interest (POIs) for the city of Singapore from Google Maps, OpenStreetMap, or comparable data sources.

2) Classify POIs into the following categories: recreational (parks, cinemas, theaters, opera houses, museums); restaurants and bars; shopping centers and shops; administrative offices (police stations, government offices); schools and universities.

3) Produce a cross-sectional data set (for one point in time, ideally, the year 2019) with longitude and latitude information on all POIs per subcategory of the above as a csv file.

4) Extract information on public transportation in Singapore from publicly available sources, and generate 1) a table (csv file) that includes all bus stops with longitude and latitude information, 2) a table (csv file) that includes all Mass Rapid Transit (MRT) stops with longitude and latitude information, 3) a table (csv file) that includes distance and travel time between all directly connected bus stops, and 4) a table (csv file) that includes distance and travel time between all directly connected MRT stops.

5) Perform spatial aggregation of the transportation networks generated in tasks 3) and 4).

6) Perform review of Matlab code that implements general equilibrium models of internal city structure. Model descriptions will be given in the form of algorithms to be implemented, and no knowledge of economics or general equilibrium models is required.

7) Perform review of Stata code that implements data processing and regression analysis.

Tasks 1) through 5) can be implemented in any programming language, though there is a preference for candidates that can execute the tasks using Python.

Candidates that do not have experience with Stata will be considered.

We are primarily looking for a master student or senior bachelor student that fulfill the skillset. Examples of relevant backgrounds include statistics, data analytics, and computer science, but students with other backgrounds are also encouraged to apply.

Languages: English

ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.