Our Mission
Reveal, Understand and Take Action
Our Vision
The vision developed at the Complex Systems Research group (NEXUS::CSR) stems from its founder's career spent wandering from pure engineering (esp. Computer Science), to hands-on field research in Environmental Sciences (esp. Ecology and Conservation), to Systems Thinking and theoretical systems modelling.
Inspired by the concept of Field Informatics, we see computerized methods, such as simulation models, embedded systems, IoT, ML/AI, as science's top-notch tools to grasp the complexity of our World. We therefore spearhead research on the applied use of such technologies to new systems and problems. Furthermore, we aim to train researchers who do not follow the hectic nature of the scientific industry, who instead take time to get out of their offices to perform research in the field, and who do not hesitate to get familiar with other scientific disciplines and methods than the one they been trained in. This is the diversity of thoughts that we aspire to!
Contemporary science is a highly specialised endeavour, which relies on experts trained on restricted fields and topics. This fragmentation of the academic landscape for the sake of efficiency and output has generated great practical progress over the past decades. Yet, modern academia has also thereby favoured silo thinking by narrowing down the breadth of knowledge and domain of action of the average scientist.
In our research group, we believe that it is necessary for scientists to be allowed to gather knowledge from various scientific disciplines to be able to bridge them, advance disruptive concepts, and develop unified theories by seeing commonalities between systems. We therefore propose the concept of "Adisciplinarity", or blindness to the boundaries between disciplines, which guides the spirit of our team.
Adisciplinary Science
From the Field
to the Lab
We realized early on that there is a strong need for scientists well-versed in advanced analytical methods and at the same time familiar with field research. Most research labs are focused on either pure hands-on research in the field (e.g. zoology labs) or on the opposite on theoretical methods (e.g. statistics labs). The former host highly valuable specific expertise and data acquisition skills, but generally lack the technological capacity to develop data acquisition technologies or perform complex systems analysis. Engineering or mathematical/statistical labs, on the other hand, are seasoned in advanced analytical methods (e.g. modelling) but very poorly grasp the limitations of the third-party data they use. Also, they most often have never experienced first hand the real-life systems they aim to analyse. The uniqueness of our research group lies in the recognition that there is a need for scientists bridging field and lab research, people who know the system intimately for having interacted with it while having a strong technological/engineering background.
Our Industrial Partners
