These PhD studies will be conducted in the framework of the interdisciplinary research project "Citizen Observatory of Water" (WeSenseIt) funded by the European Commission, which is due to start on 1 October 2012. This project will be carried out by a consortium consisting of 14 partners, namely, University of Sheffield (project coordinator), Middlesex University, Knowledge Now Limited and Doncaster Metropolitan Borough Council (United Kingdom); UNESCO-IHE, Disdrometrics and HydroLogic Research Delft (The Netherlands); École Polytechnique Fédérale de Lausanne and SensorScope (Switzerland); Advanticsys and StarLab (Spain); Quinary SpA, the River Basin Authority "Autorita’ di Bacino dei fiumi Isonzo, Tagliamento, Livenza, Piave, Brenta-Bacchiglione" (Italy); and SoftwareMind (Poland).
In order to harness environmental data and knowledge to effectively and efficiently manage water resources, WeSenseIt will develop a "citizen observatory of water" which will allow citizens and communities to take on a new role in the information chain: a shift from the traditional one-way communication paradigm towards a two-way communication model in which citizens become active stakeholders in information capturing, evaluation and communication.
The key aspect of WeSenseIt is the direct involvement of user communities in the data collection process: WeSenseIt enables citizen involvement by collecting data via an innovative combination of easy-to-use sensors and monitoring technologies as well as harnessing citizens’ collective intelligence, i.e. the information, experience and knowledge embodied within individuals and communities. The approach will be tested and validated in three case studies in water management in collaboration with water management and civil protection agencies in the UK, the Netherlands and Italy. These case studies cover the entire hydrologic cycle with a major focus on variables responsible for flood and drought occurrences.
PHD RESEARCH DESCRIPTION
In the framework of WeSenseIt project, UNESCO-IHE Institute for Water Education is initiating two PhD studies.
- Topic 1. Dynamic multi-objective optimisation of dynamic heterogeneous networks of physical and social sensors. Current theories and methods for designing monitoring networks aim to place traditional, fixed sensors to accurately infer spatial and temporal state of water systems and forecasts. Citizen Observatory of Water, however, needs to couple data from diverse sources forming a network of fixed and dynamic sensors providing physical and social data, identify the best spatial and temporal data needs, allowing dynamic sensors, such as those carried along by citizens, to complement the information coming from traditional fixed sensors and remote sensing tools. The main objective of this study will be to develop and test the mathematical and algorithmic framework for dynamic network optimization, able to identify the optimal sensor locations, variables to measure, time coverage, and reliability range in real-time providing the best possible information content, e.g., for a flood or water quality forecast. Additional tasks relate to the fact that physical and social sensors may be providing conflicting information, and this would need the selection of dedicated dynamic social sensors (humans activating the carried physical sensors, or sending verbal information) to be requested to visit particular locations of interest to resolve a conflict. Multi-objective optimization methods to be tested and refined will include methods developed in the area of computational intelligence (evolutionary and adaptive random search algorithms), made however more robust due to uncertain data, and more efficient due to the use of computationally intensive models. In computing, use of parallelization using clusters and cloud computing is foreseen.
- Topic 2. Optimal integration of heterogeneous uncertain data into models. The increasing data availability and the current availability of significant computing power makes it possible to realise truly uncertainty-aware (and hence, risk-based) adaptive modelling. Current water modelling technologies typically do not use heterogeneous data sources, largely unable to account for uncertainty and trust/value of data and are not geared towards producing outputs to be visualised in hand-held devices. This PhD task aims at developing new adaptive modelling procedures, focusing on optimal model design and updating that use heterogeneous data sources coming from sensors of different types, ranging from physical to social, from fixed to dynamic, and from point to distributed, including the remote sensing data. Most of these sensor data have varying information value, (un)certainty, life-span and space-time coverage, and this makes the model optimization a challenging task. This work will also include development and appraisal of more efficient multi-objective robust optimization methods (in cooperation with the other PhD), use of predictive models of uncertainty (fuzzy and/or probabilistic) based on computational intelligence methods, and the possibility of making uncertainty-aware modelling part of decision making in water management.
Both PhD studies will be conducted in close cooperation. Decisions on allocating the topics to candidates will be made on the basis of their background, experience and aspiration.
The promoter (supervisor) of these PhD studies will be Prof. Dimitri Solomatine, and the mentor – Dr. Leonardo Alfonso, with the involvement of other staff. The research will be carried out at the premises of UNESCO-IHE in Delft, The Netherlands. The PhD position is funded by a fellowship that covers the tuition fee, health insurance, travel, visits to conferences and the monthly tax-free allowance of approximately 1200 Euros a month (or 100 or 200 Euros higher depending on the composition of the family). Yearly performance appraisal will be conducted. The expected starting date is 15 October 2012 and the duration of the project is 4 years.s.
The candidates should have:
- MSc degree (average mark: 80% or above) in a discipline relevant to the topic (e.g., Applied Mathematics, Civil Engineering, Computer Sciences)
- experience in hydraulic / hydrological modelling
- some experience in computer coding in MATLAB, Python, R, C++, Java, Delphi or other languages
- high proficiency in English (written and spoken); additional language skills in Italian and Dutch would be of some advantage. On details, please see http://www.ihe.nl/Education/Prospective-Students/English-language-requirements
- motivation to work in a multidisciplinary and multicultural environment.
Applications in English should be sent by email to Ms. Jos Bult (email@example.com), secretary of the department (copy to: firstname.lastname@example.org, email@example.com). Your application should be in one PDF file with your family name as the file name, and include: 1) the motivation letter; 2) curriculum vitae, with the names and contact details of three contactable referees; 3) copy of the MSc diploma with the transcript of marks in English.
In your motivation, please also rank the two mentioned PhD topics according to you interest. Please mention ”WeSenseIt project, PhD on Networks and Models”, in the subject line of your email.
The positions are open until filled. First review of application materials will be 6 September 2012. Short-listed candidates will be contacted by 16 September 2012. It is expected that the candidate will be selected by October 1, 2012.
DEPARTMENT AND CHAIR GROUP
Department of Integrated Water Systems and Governance provides post-graduate education and training in the areas of water management and hydroinformatics to professionals with backgrounds in engineering, the natural and social sciences and management, carries out research in these fields and supports the development of education and research capacity in universities and other knowledge centres in developing countries and countries in transition.
The Chair Group of Hydroinformatics consists of 9 staff and 14 PhD students and focuses in its research on various aspects of hydroinformatics – advanced modelling, computational intelligence, optimization, decision support, internet-based computing, with applications to various water-related problems.