Lourens has worked at the Netherlands eScience Center as an eScience Research Engineer since early 2016. He currently works on the e-MUSC and SECCONNET projects. In e-MUSC, he works on theory and tools for doing uncertainty quantification and sensitivity analysis of large, compute intensive multiscale models, with applications in biomedicine as well as other fields. In SECCONNET, he is designing a system for distributed execution of scientific analyses (including workflows and machine learning) in the presence of legal, ethical and/or business-imposed constraints on the data and software. Previously, he worked on the Enhancing Protein-Drug Binding Prediction project, where he created software for doing large-scale molecular dynamics simulations for use in drug discovery.

Before joining the eScience Center, Lourens worked at the Computational Geo-Ecology group of the University of Amsterdam, where he led the design of the data model and system architecture of the Dutch National Database of Flora and Fauna (NDFF). The NDFF is a repository for species observations, in which volunteer and professional observers record observations. The system is used by governments to ensure compliance with EU nature protection directives, by scientists for modelling species distributions, and by companies operating in the Dutch landscape to assess potential risks to nature and any needed mitigations.

After this, he was a PhD candidate, working on the incorporation of dispersal limitations into species distribution models, and fitting such models to observation data using Bayesian techniques. He also worked on processing high-resolution (30m / 600 gigapixel) global forest cover data into statistics on forest loss and fragmentation, with the goal of investigating at which scales ecosystem services are most affected by these changes.

Lourens studied Computer Science at the University of Twente in The Netherlands, where he received an MSc (Hons.) in Databases and Information Systems.

Key skills

  • High-performance programming
  • C++ programming
  • Bayesian methods
  • Databases and data models
  • System architecture
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