The Basque Centre for Climate Change (BC3) is looking for candidates who can support its strategic activities related to integrated data science and collaborative, integrated modeling on the semantic web. The selected candidates will contribute to the ARIES (ARtificial Intelligence for Ecosystem Services) project powered by the k.LAB software stack, a semantic web infrastructure that uses artificial intelligence to build computational solutions to environment, policy and sustainability problems. The open source k.LAB software includes client and server components that connect data and models from distributed repositories, guided by machine reasoning over a set of shared ontologies. This technology, based on machine reasoning, machine learning, distributed computing and high-performance, multi-disciplinary and multi-paradigm system modeling, is the flagship product of the Integrated Modelling (IM) Partnership (http://www.integratedmodelling.org) which is expected to serve a growing number of worldwide users (from academia, governments, NGOs and industry) in the years to come.
Candidates could match any of three professional profiles dedicated to different components of the k.LAB technology:
- Back-end developer. The candidate will contribute to designing the REST API and its implementation for the modeling and communication services that coordinate distributed computations and queries for data and model components driven by semantics. The skills required involve Java and Spring technologies, experience with authentication, load balancing and distributed computation, with an eventual involvement of Spark or comparable distributed data platform.
- Modeling engine (middleware) developer. The candidate will contribute to the design and implementation of the modeling engine, which assembles network-available model components and data and compiles the assembled graph into a runnable dataflow. The candidate should be conversant with simulation modeling principles, machine reasoning using OWL and its Java implementations (OWLAPI), open source GIS (e.g. Geotools), machine learning (Weka), and be aware of, or open to quickly learn, corresponding technologies on the Java platform. Understanding of REST, Spring and Websockets (for communication with the front-end) will be necessary.