Oferta de trabajo: Modeller and remote sensing expert para el proyecto ARIES (ARtificial Intelligence for Ecosystem Services) impulsado por k.LAB software stack


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.

Position:  Modeller and remote sensing expert 

A scientific/technical project officer (predoctoral) or a postdoctoral position in ecoinformatics and modelling of coupled human-environmental systems using earth observation and machine learning techniques. The candidate should have a strong background in remotely sensed data processing techniques, modelling and machine learning.


Key responsibilities:

  1. Collaborate in building, evaluating and delivering global integrated models within the ARIES platform;
  2. Collaborate in building, evaluating and delivering complexity oriented models of coupled human-environmental systems;
  3. Integrate such models and their results within a holistic, integrated trade-off assessment framework for decision- and policy-making;
  4. Assist and teach in training and educational activities connected with the ARIES project, such as the International Spring University on Ecosystem Services Modelling and other worldwide case studies;
  5. Publish high-impact, peer-reviewed research in international scientific journals in ecoinformatics and environmental decision-making.

Main requirements:

  1. 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 strong programming skills (preferentially in Java or other object-oriented languages) and a working knowledge of GIS and OGC standards.
  2. Familiarity with any of the following technologies is an asset: Git, GeoServer, Linux, RESTful web services, openCPU, Google Earth Engine, R/Renjin, Protocol Buffers, JSON. Being initiated to ontologies, artificial intelligence, and machine reasoning is desirable. Familiarity with any of the following methods is an asset: agent-based modelling, network analysis, multi-criteria analysis, Bayesian network modelling, stakeholders’ participation and cognitive mapping.
  3. 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.


Term of contract

The position will be for a period of 15-18 months (with possible extension)


The position will carry competitive salary, matching the academic and professional profile of the applicant, and excellent work conditions.


Basque Centre for Climate Change (BC3), Biscay (Spain).

Interested candidates should send their CV, preferably in English, by electronic mail to info@integratedmodelling.org. Informal enquiries can be made to Prof. Ferdinando Villa (Ferdinando.villa@bc3research.org), and Stefano Balbi (stefano.balbi@bc3research.org). All information received during this process will be handled confidentially.


1st of September

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María de Maeztu Excellence Unit 2023-2027 Ref. CEX2021-001201-M, funded by MCIN/AEI /10.13039/501100011033

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