PhD Student

Job description

The water sector is under increasing pressure worldwide. To ensure water security and sustainable development, major paradigm shifts are in progress. A first important shift is the transition towards a circular water and resource economy. This transition is inherently a multidisciplinary and multi-objective effort that implies bridging historical silo’s (for example drinking water, wastewater, hydrology for rainwater harvesting, agriculture, industry).
The second paradigm shift is the digital transformation and the emergence of powerful and automated model simulations that support real-time smart decision making.

However, to achieve powerful digital tools that can support holistic decision making towards more circular and sustainable water management, many different data sources, models and sub-models need to communicate with each other in a more efficient and automated way.
For example, the development of a digital twin for water management in a city requires heterogeneous sources of data (e.g., water quality, energy, cost, meteorology, etc.) and various models/sub-models (e.g., models for sewer networks, hydrology, river hydraulics, water/wastewater treatment technologies, economic models, etc.) of the system to be connected to each other and communicate in an efficient and automated manner.

WaterFRAME (Water Fit for Reuse Architecture and Modelling Ecosystem) is a FWO Strategic Basic Research (SBO) project that aims at developing an interoperable holistic digital framework for the water domain that can bridge historical data and modelling silos to support high-level decisions on water management. The WaterFRAME decision support system will be realized through standardized data formats and the concept of ontologies (a conceptual structure of a domain of interest including all objects and the semantic relationship between them). A library of ontologies will be developed based on specific use cases that encompasses all data, information, models, parameters... within the water reuse domain. Data and models will be then mapped onto the ontologies to create knowledge graphs which allows to create a flexible, interlinked and dynamic data representation of the real-world which can serve as a digital twin of the system under study. An optimization layer will be developed on top of the dynamic knowledge graph that will interact with the domain ontologies to provide support for decision making through data extraction, analysis, model simulations and automatic optimization. The knowledge graph will moreover be augmented with environmental indicators through LCA analysis.
The methodology developed within WaterFRAME will be applied to 3 use cases with in-silico and real datasets for a proof-of-concept implementation of the holistic decision-making framework.

In this position, you will be working in a dynamic research group and will be actively involved in state-of-the-art research on mathematical modelling, integrated assessment and interoperability. You will contribute to development of an ontology model of the water reuse domain within the context of the WaterFRAME project as well as a dynamic knowledge graph that will serve as the input for an optimization layer (developed within another work package of the project) to provide quantitative indicators for holistic decision making.

Job profile

• You will conduct a literature review on the existing ontologies and semantic models for the water and water-related sectors at local and global levels.
• You develop ontologies for the water reuse domain such as ontology for rainwater collection and storage, ontology for treatment processes, ontology for water reuse cases (e.g., wastewater effluent, drinking water, gray water), ontology for mathematical models within the water reuse domain.
• You develop a library of interconnected ontologies based on the one developed in the previous task.
• You develop methods for aligning the ontologies to check for semantic, syntactic and structural heterogeneity. You will investigate the use of machine learning algorithms for automatic ontology alignment.
• You will construct a dynamic knowledge graph by mapping sample (virtual and real) datasets to the library of ontologies and test the ontology model for different case studies..
• You will collaborate closely with the project partners, gathering knowledge needed for development of a comprehensive and relevant library of ontologies.
• You write down the results in scientific articles and a PhD thesis.
• You have solid experience with computer programming (such as Python/Julia/C/C++/etc.) including object-oriented programming paradigm
• You have good knowledge of mathematical modelling, data analysis and machine learning or a strong affinity to learn
• Background in semantic data models is a plus
• Knowledge in modelling and integrated assessment of water and wastewater systems is a plus
• You like a challenge and are not afraid to learn and explore new methodologies
• You are a quick learner and can conduct independent research
• You are fluent in English, both speaking and writing
• You are motivated and dedicated
• You are a team player

WHAT WE CAN OFFER YOU

• We offer a full-time position as a doctoral fellow, consisting of an initial period of 12 months, which - after a positive evaluation, will be extended to a total maximum of 48 months.
• The fellowship amount is 100% of the net salary of an AAP member in equal family circumstances. The individual fellowship amount is determined by the Department of Personnel and Organization based on family status and seniority. A grant that meets the conditions and criteria of the regulations for doctoral fellowships is considered free of personal income tax. Click here for more information about our salary scales
• All Ghent University staff members enjoy a number of benefits, such as a wide range of training and education opportunities, 36 days of holiday leave (on an annual basis for a full-time job) supplemented by annual fixed bridge days, bicycle allowance and eco vouchers. Click here for a complete overview of all the staff benefits (in Dutch).

How to apply

Your application must include the following documents:

• Your CV: an overview of your publication list and your study results (merged into one pdf file)
• A Cover letter: your application letter in PDF format (describe your values and provide examples relevant to the job description)
• Diploma: a transcript of the required degree (if already in your possession). If you have a foreign diploma in a language other than our national languages (Dutch, French or German) or English, please add a translation in one of the mentioned languages.
  
  

As Ghent University maintains an equal opportunities and diversity policy, everyone is encouraged to apply for this position.

Please send your complete applications by email to Prof. Elena Torfs (elena.torfs@gci.ulaval.ca) and Dr. Saba Daneshgar (saba.daneshgar@ugent.be).