PhD Position in ML-Based Interpretation of Geophysical Legacy Data for Mineral Exploration

Delft; Delft; On-site; EUR 36,000 - 45,000; Full time

Job summary

TU Delft offers a PhD position focusing on ML-based interpretation of geophysical legacy data for mineral exploration. You will develop and test new methods to integrate legacy seismic and well-log data with basin modelling, geochemistry and machine learning to map the Kupferschiefer horizon in the Netherlands and the North Sea.

The model will combine geophysical interpretation, basin analysis and multimodal data integration, with a focus on sequential data and physics-informed ML.

Qualifications

• MSc degree in geophysics, geology, earth sciences, geological or petroleum engineering.
• Experience in scientific programming (Python or Matlab).
• Good writing and communication skills.

Responsibilities

• Develop and test new methods for deep exploration using ML-based interpretation of legacy seismic and well-log data.
• Integrate seismic, well-log, basin modelling, geochemistry and ML for mineral exploration.
• Contribute to multimodal and physics-informed ML developments and data integration.

Education

MSc degree in geophysics, geology, earth sciences, geological or petroleum engineering

Tools

Python

MATLAB

Job description

PhD Position in ML-Based Interpretation of Geophysical Legacy Data for Mineral Exploration

Develop and test new methods for deep exploration of sediment-hosted copper systems by integrating legacy seismic and well-log data with basin modelling, geochemistry and machine learning.

We are looking for a PhD candidate who is keen to explore how legacy seismic and well-log data acquired for petroleum exploration can be transformed into a new resource for deep mineral exploration. The candidate will combine quantitative geophysical interpretation, basin modelling and machine learning to investigate a challenging target: the thin Kupferschiefer horizon buried beneath the Netherlands and the North Sea.

The Dutch subsurface offers an exceptional dataset of more than 6,000 wells, including approximately 1,700 in which the Kupferschiefer has been identified, together with extensive high-quality 2D and 3D seismic reflection data and available cores. Yet these data were acquired and interpreted for hydrocarbon exploration. Recognizing and mapping a horizon a few metres thick at kilometers depth, and identifying features associated with its mineral potential, requires methods that can extract information across datasets with very different resolution, coverage and physical meaning.

The goal of this PhD project is to develop an integrated methodology for characterizing the Kupferschiefer and its geological context. You will analyse well logs to identify the target horizon and extrapolate it to wells where it has not yet been recognized. You will use seismic data for cross-well correlation and improved spatial characterization, and basin analysis to investigate the roles of stratigraphy, faulting and basin evolution. Geological, petrophysical, geochemical and mineralogical information will be integrated to identify characteristics and fingerprints associated with copper mineralization.

Machine learning will be an important part of this methodological development. You will establish robust and interpretable baseline models for well-log analysis and investigate approaches designed for sequential data, including convolutional and transformer-based models. You will also contribute to multimodal methods that combine seismic, well-log, geological and geochemical information while incorporating geological knowledge and physical constraints. A postdoctoral researcher will work alongside you for one year, focusing primarily on advanced multimodal and physics-informed machine-learning developments.

The research is part of the EU-funded INTELLIGENT project. Results from the Dutch basin will be combined with complementary studies by other groups working on the Kupferschiefer and related systems to develop a regional-scale mineral-system model and transferable exploration workflows. Your multidisciplinary research work will be carried out within the Applied Geophysics and Petrophysics Section and the Resource Engineering Section of GSE at TU Delft. Through close collaboration with TNO, the National Materials Observatory and partners from universities, geological surveys, research institutes and industry across Europe, you will test new methodological ideas within an international network and against a real, societally relevant exploration challenge.

Job requirements

Being creative and independent, you have the ability to conduct independent research and collaborate across geoscience, engineering and data-science disciplines.

You also have:

• An MSc degree in geophysics, geology, earth sciences, geological or petroleum engineering, or a closely related discipline.
• Interest in applied geophysics and resource exploration and machine learning methods.
• Experience in scientific programming (preferably Python or Matlab).
• Good writing and communication skills.

Experience in one or more of the fields below is considered an advantage:

• Quantitative interpretation of seismic reflection data and well logs.
• Petrophysics, low temperature geochemistry and mineralogy.
• Machine Learning (deep learning and/or statistical learning methods).

TU Delft (Delft University of Technology)

Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow.These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional.Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty of Civil Engineering and 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 conductedin 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.

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€3204 - €4051 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

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.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service , offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

#EU funded: This is an EU funded project, named INTELLIGENT: Innovative exploration technologies for deep-targeting critical mineral deposits, Horizon Europe (HORIZON) Call: HORIZON-CL4-2026-01, Project: 101345448, Coordinator Uppsala University.

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 .

Please note:

• You can apply online. We will not process applications sent by email and/or post.
• As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest.You can find more information about this assessment on our website about knowledge security.
• Please do not contact us for unsolicited services.

Faculty/Department: Faculty of Civil Engineering and Geosciences

FTE: 1,0

Submission is possible until: 17 Nov 2026