Are you interested in spatial optimisation algorithms? And do multi-objective optimisation problems in spatial planning and economic geography draw your attention? Do you advocate for free and open source software? Do you program in Python and/or R? Then this job at the department of Human Geography and Spatial Planning (Utrecht University, The Netherlands) is perfect for you!

The transition towards a sustainable and livable urban future requires resolving multiple issues. Potential solutions for these issues may be compatible (synergies), or may conflict (trade-offs). For example, under the current pressing housing demand, the objective to maximise peri-urban landscape biodiversity and food provision results in high residential densities within the existing urban fabric. This may conflict with the objectives to minimise the decline of open space and the congestion and air pollution in the city. Quantifying synergies and trade-offs between these objectives can serve the spatial planning process. Multi-objective spatial optimisation algorithms offer such quantification.

The aim of this project is to improve the comparability and selection of spatial optimisation algorithms through the design, testing, and publication of spatial optimisation benchmarks. Benchmark problems or benchmarks are standardised tests used in the computer sciences for the evaluation, characterisation and performance measurement of algorithms, software packages, or hardware.