During the past decade, the RL has envisioned and built the ARIES (ARtificial Intelligence for Environment and Sustainability (https://aries.integratedmodelling.org/ ) platform, a technology that integrates network-available data and model components through semantics and machine reasoning.
Its underlying open-source software (k.LAB, https://docs.integratedmodelling.org/technote/ ) 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.
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).
The selected 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), 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.
Initial Tasks:
- Getting familiar (6 months) and proficient (12 months) with the functioning of the k.LAB modelling engine and assist the team in debugging and resolving potential modelling and contextualization issues.
- Contribute to further development of the k.LAB modelling engine (12 months).
- Understand contribute and resolve issue related to k.LAB nodes and adapters* components (12 months).
Implied subtasks:
- Build the software and navigate the source code
- Interpret the source code
- Understand how the k.LAB modelling engine works
- Profiling code execution
* Diverse, extendible sourcing of information for resources is enabled through the use of adapters, software plug-ins that adapt a specific data or service format to the API. The adapter identifier and parameters are specified in the metadata associated to the URN and used to select the methods for contextualization, import, export and indexing. Adapters are made available as k.LAB components, installable in k.LAB Engines and k.LAB Nodes, and can be extended by developers using the Java API to support formats and services not yet available. External APIs (e.g. datacubes) can be supported by deploying a bridge adapter to one or more k.LAB Nodes.
(https://docs.integratedmodelling.org/technote/index.html#_resource_adapters )
After 12 months the candidate is expected to:
- Collaborate on developing, strengthening and debugging the back-end and/or the client components of the k.LAB software stack (and more specifically the modeling engine).
- Collaborate on the definition of unit tests and code review policies for both k.LAB and the associated data/model products.
- Participate in all aspects of the development life cycle including analysis, design, development, documentation, release and deployment.
- Communicate and coordinate with both technical and non-technical stakeholders.
