PhD position within Risk-Based Asset Management of Urban Water Systems
Deadline: 03.03.2025
Publisert
NMBU will contribute to securing the future of life through outstanding research, education, communication and innovation. We have the country's most satisfied university students, who receive research-based education in a unique student environment. Our graduates gain a high level of competence in interdisciplinary collaboration and are popular in the labor market.
NMBU has internationally leading research environments in several subjects. Together with our partners in society and business, we contribute to solving some of the biggest societal challenges of our time. We focus on innovation, communication and entrepreneurship because we believe these challenges are best solved with joint efforts. We believe that a good working environment is characterized by diversity.If necessary, workplace adaptations will be made for persons with disabilities. More information about NMBU is available at www.nmbu.no/en.
About The Faculty of Science and Technology
The Faculty of Science and Technology (REALTEK) develops research-based knowledge and educates civil engineers and lecturers needed to reach the UN's sustainability goals. We have approximately 150 employees, 70 PhD students and soon 1500 students. The education and research at REALTEK cover a broad spectrum of disciplines.
This includes data science, mechanics and process engineering, robotics, construction and architecture, industrial economics, environmental physics and renewable energy, geomatics, water and environmental engineering, applied mathematics as well as secondary school teacher education in natural sciences and use of natural resources such as in agriculture, forestry and aquaculture. The workplace is in Ås, 30 km from Oslo.
What is it really like to work at the Faculty of Science and Technology (REALTEK) at NMBU?
Effective infrastructure asset management is critical for ensuring the long-term sustainability and reliability of urban water systems, especially water distribution networks. Many countries, including Norway, are facing the challenge of aging infrastructure, where key components, such as pipes, are nearing the end of their useful life. In addition to operational factors, environmental conditions like cold climates can accelerate the deterioration of water infrastructure, further reducing its useful life. As infrastructure ages and faces growing pressures from population growth, climate change, and urban expansion, effective asset management that minimizes costs and risks while maximizing value is more critical than ever. Risk-based asset management provides a powerful and cost-effective approach to prioritize and optimize the maintenance, repair, and replacement of key components within water supply systems, reducing the likelihood and consequences of failures, minimizing leakage, and improving overall system performance.
This PhD position presents an exciting opportunity to advance the field of asset management for urban water distribution networks by leveraging Artificial Intelligence (AI) including predictive machine learning models, risk assessment techniques, and multi-objective optimization. The research will integrate AI-driven predictive and prescriptive approaches, focusing on developing advanced data-driven methods to predict pipe failures and assess associated risks, including economic, environmental, and social factors. The goal is to generate optimized maintenance plans that harness AI capabilities to not only forecast asset performance but also prescribe the most effective strategies for repair, replacement, and rehabilitation, considering factors such as cost, risk, leakage, and system performance.
Possible areas of research include:
Developing predictive machine learning models to forecast asset failures and estimate the remaining useful life of pipes.
Quantifying failure consequences (e.g., Multi-Criteria Decision Analysis) and designing risk-based asset management frameworks to evaluate system vulnerability and prioritize critical infrastructure for maintenance.
Applying multi-objective optimization techniques to design effective maintenance strategies, including repair, replacement, and rehabilitation, while balancing cost, risk, and system performance.
Validating the proposed methods using real-world data to ensure practical applicability and effectiveness in urban water systems.
Depending on the successful candidate’s interdisciplinary background in urban water systems, machine learning, and asset management, the above areas of interest will be further refined and adjusted in a relevant and feasible research plan. The candidate will work actively as part of the Water Transport Systems and Stormwater Management research group. This position offers the opportunity to contribute to the development of innovative, risk-based asset management solutions that will help improve the performance, resilience, and sustainability of water networks.
You will join the research efforts at the Department of Building and Environmental Technology at NMBU and contribute to its development. As a student in the PhD Programme Science and Technology, you will work towards your doctoral thesis and earn your PhD upon successful defense of your thesis.
Main tasks
Description of tasks or PhD project:
Conduct a comprehensive literature review and state-of-the-art analysis on risk assessment, asset management, predictive modeling, and optimization in urban water distribution networks.
Develop and apply machine learning and survival analysis models to predict asset failures and assess the useful life of infrastructure components.
Design and implement a risk-based asset management framework to prioritize maintenance strategies for water distribution systems.
Apply multi-objective optimization techniques to generate effective maintenance plans.
Collaborate with research teams, industry partners, and stakeholders to validate methods using real-world case studies and data.
Publish at least 3 high-quality publications in internationally scientifically indexed journals.
Present research findings at national and international conferences
Competence
Required qualifications
A Master's degree or equivalent in Civil Engineering, Water Engineering, Wastewater/Environmental Engineering, or a closely related field. Foreign degrees must correspond with the admission criteria for the PhD program. Candidates submitting MSs thesis within 31. May 2025 may be considered.
Personal suitability and motivation for the position.
The following experiences and skills will be emphasized:
Strong knowledge of risk assessment and asset management
Experience in machine learning, predictive modeling, and optimization techniques.
Good experience with urban water network modeling and related tools (e.g., EPANET, GIS).
Proficiency in programming and developing codes (e.g., Python, R, MATLAB).
A proven track record of relevant publications in internationally recognized scientific journals is an advantage.
Applicants who have recently graduated with excellent results may be given preference.
Personal qualities:
Ability to work independently and motivated to collaborate in an interdisciplinary team
Strong communication skills (English required; Norwegian is considered an advantage)
Excellent social and collaborative skills
Strong problem-solving abilities and a proactive approach to research challenges
The successful applicant must meet the conditions defined for admission to a PhD programme at NMBU. For more detailed information on the admission criteria please see the PhD Regulations and the relevant PhD programme description.
Remuneration and further information
We offer:
Salary 532200 NOK per annum. For exceptionally well qualified candidates a higher salary may be considered.
Government pay scale position code 1017 PhD Research Fellow.
The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NMBU.
Contact information
For further information, please contact:
Dr. Abbas Roozbahani, Associate Professor, E-mail: abbas.roozbahani@nmbu.no; (do not use this e-mail for application, it is only for questions)
To apply online for this vacancy, please click on the 'Apply for this job' button above. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
Application deadline: 03.03.2025
Please note that all documents should be in English or a Scandinavian language.
By applying the candidate confirms that information and documentation submitted via the job application can also be used by NMBU in a possible admission process to the PhD program.
Interviews with the best qualified candidates will be arranged. Applicants invited for an interview are expected to present original diplomas and certificates.
The application must include:
Motivation letter (maximum 1 page)
Curriculum Vitae (with a list of education, positions, teaching experience, administrative experience and other qualifying activities, including a complete list of publications)
Certified copies of academic diplomas and certificates. (i.e. Diploma, transcript. Diploma supplement for both bachelor and master). Diplomas, transcripts and diploma supplements that are not in Norwegian or English must be uploaded in the original language. An English translation of these documents must also be attached.
Applicants from universities outside Norway are kindly requested to send a diploma supplement, or a similar document, which describes in detail the study program and grading system.
Additional relevant documentation of professional knowledge (for example, list of scientific works). If it is difficult to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included.
