PhD Researcher: AI-Driven Smart Water Systems

Newcastle University; Newcastle upon Tyne; On-site; GBP 48,000 - 50,000; Full time

Benefits offered by this job

Mobility allowance

Family allowance (eligibility)

Health and wellbeing initiatives

Job summary

Newcastle University is inviting applications for a fully funded PhD position as part of the i3WaterS network. The project aims to develop data-driven methods and digital twins for smart water distribution systems, training 15 doctoral candidates across Europe.

The successful candidate will join an international supervisory team and work on DIAS, leveraging sensor data, operational conditions, and digital twins to enhance resilience and efficiency.

Qualifications

• Strong interest in water distribution systems, smart water infrastructure, or infrastructure management.
• Ability to undertake quantitative and computational research.
• Good analytical and problem-solving skills.
• Experience or potential in programming using Python, R, Julia, MATLAB, or related language.
• Ability to work with complex datasets and computational models.
• Clear written and spoken English.

Responsibilities

• Develop and implement a District Information Area framework based on network connectivity, data sharing, and operational interdependencies.
• Investigate adaptive or virtual subnetworks complementing existing District Metered Areas.
• Develop methods for semi-autonomous local decision-making with coordinated system-wide operation.
• Integrate sensor, smart meter data, models, and neighbour area information to assess local infrastructure condition.
• Apply AI, network science, and decision-support methods to water distribution management.
• Publish research findings in peer-reviewed journals and present at selected conferences.

Skills

Python

R

Julia

MATLAB

Data analysis

English proficiency

Education

2:1 Honours degree

Job description

Newcastle University is inviting applications for a fully funded PhD position as part of the i3WaterS network. The project aims to develop data-driven methods and digital twins for smart water distribution systems, training 15 doctoral candidates across Europe.

The successful candidate will join an international supervisory team and work on DIAS, leveraging sensor data, operational conditions, and digital twins to enhance resilience and efficiency.