During the past decade, the RL has envisioned and built the ARIES (ARtificial Intelligence for Environment and Sustainability) platform, a technology that integrates network-available data and model components through semantics and machine reasoning.
Its underlying open-source software (k.LAB) handles the full end-to-end process of integrating data and with multiple model integration types to predict complex change. It also supports selection of the most appropriate data and models using cloud technology and following an open data paradigm: the resulting insight remains open and available to society at large, and becomes a base for further computations, contributing to an ever-increasing knowledge base. For the first time, it is possible to consistently characterize and publish data and models for their integration in predictive models, building and field-testing technologies that have eluded researchers to date.
The OBServ project focuses on building predictive models of pollinator biodiversity and ecosystem function delivery using both data-driven and mechanistic models.
We are looking for an individual who can support strategic activities related to integrated data science and collaborative, integrated modelling on the semantic web (semantic meta-modelling).
Job description: Contribute to the ARIES (ARtificial Intelligence for Environment and Sustainability) platform, a semantic web infrastructure that uses artificial intelligence (AI) to build computational solutions to environmental, policy and sustainability problems. This technology, based on machine reasoning, machine learning, distributed computing and high-performance, multi-disciplinary and multi-paradigm system modelling, is the flagship product of the Integrated Modelling Partnership (IMP) which is expected to serve a growing number of worldwide users (from academia, governments, NGOs and industry) in the years to come.
ARIES’ current model resources largely focus on ecosystem services, using diverse modeling paradigms including machine learning and deductive models. The modeler will work as part of a team to develop and test new models that expand the breadth of ARIES’ model library, including ecosystem services and other environmental models at scales from local to global. Data-driven models built with a variety of machine learning classifiers have been applied so far to land cover change modelling, biodiversity modelling (https://www.sciencedirect.com/science/article/pii/S2212041617306423), water quality modelling and pollination modelling. The candidate will work to expand the use of ML libraries (e.g., Weka) and applications beyond the state of the art.
The position may require international travel on an as-needed basis.
- Collaborate in building, evaluating and delivering integrated models within the ARIES platform. The OBServ project focuses on pollination as a central piece for the modelling and simulation of agri-systems and biodiversity, but the position will entail multi-disciplinary applications;
- Collaborate in building, evaluating and delivering complexity-oriented models of coupled human-environmental systems;
- Integrate such models and their results within a holistic, integrated trade-off assessment framework for decision- and policy-making;
- The applicant must have a degree in computer science, ecology, geography, engineering, or other fields of relevance to ecoinformatics. A very strong background in computational modelling is required, along with programming skills (any language and in particular Python, Java, R and Julia).
- Familiarity with any of the following methods is an asset: agent-based modelling, network analysis, Bayesian network modelling, system dynamics. Being initiated to ontologies, artificial intelligence, and machine reasoning is desirable. Familiarity with any of the following technologies is an asset: Git, GeoServer, Linux, RESTful web services, openCPU, JSON.
- The applicant must have excellent interpersonal and communication skills. Excellent written and oral command of English is required. An ability to work in teams and experience in the use of collaborative software platforms and distributed version control systems are necessary.
- Applications including previous modelling works and their documentation in an online repository will be given priority.