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Job advertisement PhD Position Surrogate-enabled Uncertainty Quantification for Water Quality Modeling

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PhD Position Surrogate-enabled Uncertainty Quantification for Water Quality Modeling

Position
PhD Position Surrogate-enabled Uncertainty Quantification for Water Quality Modeling

Employer
Delft University of Technology logo

Delft University of Technology

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Civil Engineering & Geosciences

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

Homepage: https://www.tudelft.nl


Location
Delft, Netherlands

Sector
Academic

Relevant divisions
Biogeosciences (BG)
Hydrological Sciences (HS)

Type
Full time

Level
Entry level

Salary
Open

Required education
Master

Application deadline
15 September 2024

Posted
1 August 2024

Job description

Challenge: Predict reactive processes affecting groundwater quality
Change: Quantify uncertainty of complex water quality models using surrogate modeling
Impact: More reliable and safe water quality management

Job Description

Reactive transport models are essential for assessing groundwater quality and contamination risks, which are critical for safe drinking water. However, uncertainties in model parameters and data hinder accurate predictions. Traditional uncertainty quantification (so-called Bayesian) methods require extensive computational resources, hindering robust uncertainty quantification for complex models.

This PhD position offers an exciting opportunity to address this challenge by leveraging surrogate modeling techniques. Surrogate models approximate computationally intensive models at reduced cost, enabling uncertainty quantification despite time budget constraints. Recent advances in machine learning techniques also offer promising opportunities to improve surrogate modeling approaches. In this project, you will develop surrogate models tailored to reactive transport models and integrate them into an uncertainty quantification framework.

Key responsibilities:
  • Design and implement surrogate modeling strategies for efficient uncertainty quantification of reactive transport models.
  • Evaluate and propagate surrogate model errors to improve uncertainty estimates.
  • Apply the developed framework to real-world case studies in collaboration with international working groups in the field of water quality modeling.

At the intersection of hydrogeology, biogeochemistry and statistics, this project bridges method development with applications in water quality modeling and process understanding. Our department brings together experts in water quality and reaction processes, mathematical modeling of environmental systems, and statistical and machine learning techniques in water resources. You will also collaborate with AIdroLab, part of TU Delft AI, and the Statistical Model-Data Integration group at the University of Stuttgart.

Requirements

  • Very good Master’s degree in a relevant field such as environmental science, environmental engineering, hydrogeology, physics, or applied mathematics.
  • Strong interest in uncertainty quantification, Bayesian statistics, and machine learning techniques. Demonstrated experience in at least one of these areas is a plus.
  • Familiarity with numerical modeling and scientific programming, for example using Python, Julia or R.
  • Experience in modeling reactive transport in groundwater is preferred.
  • Very good written and spoken English as you will be working in an international community.
  • Enthusiasm for interdisciplinary research involving experts from hydrogeology to statistics.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.


How to apply

Interested in this position? Please apply no later than 15th of September 2024 in the application portal and upload the following application materials:

  • A motivation letter, including (1) a brief personal introduction, (2) an explanation of how your previous studies and experience have prepared you for this PhD position, and (3) why you are interested in this position. The maximum length is one page.
  • 1-2 page CV including names and contact information of two references.
  • Copies of academic transcripts, including grades of all courses taken.
  • An example of a scientific text you wrote individually, for example your MSc thesis or a course report (in English)

We expect to conduct job interviews in late September and October 2024. You can apply online. We will not process applications sent by email or mail. A pre-employment screening can be part of the selection procedure.

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