Key Skills

Job Description

Job Description About the position The University of Twente wants to be an organisation that optimally deploys diversity, talent, and capabilities in the labour market, now and in the future. In line with our diversity and inclusiveness policy, we strongly encourage people with a (work) disability to apply for this position. The University of Twente is committed to providing a working environment where everyone is valued, respected, and supported to progress. Our priority is to ensure that no one is disadvantaged based on their ethnicity, gender, culture, disability, LGBTQ+ identities, family and caring responsibilities, age, or religion. We encourage everyone who shares these values to apply. Your challenge The transition toward sustainable offshore resource management and subsurface utilization is driving an unprecedented demand for reliable monitoring technologies. Offshore environments present unique challenges for geophysical observations due to their limited accessibility, high operational costs, and sparse instrumentation. At the same time, the need to detect and characterize seismic activity in near real time is becoming increasingly important for ensuring operational safety, understanding subsurface processes, and supporting informed decision-making in offshore settings. Recent advances in sensing technologies offer new opportunities to improve monitoring capabilities and provide a more comprehensive view of dynamic processes occurring beneath the seabed. This project will develop innovative methodologies to integrate In-Well Distributed Acoustic Sensing (In-Well DAS) data with observations from offshore seismological stations, currently under development, enabling on-site near-real-time offshore seismic monitoring. The existing In-Well DAS fiber-optic cables can be transformed into dense arrays of seismic sensors, complementing the offshore seismic stations and potentially increase the detection limits. By combining these complementary observations through advanced signal processing, machine learning, and physics-based simulations, the project aims to improve event detection, localization, and characterization capabilities in challenging offshore environments. The resulting framework will establish scalable workflows for seismicity monitoring applicable to offshore regions. Results will be disseminated through high-quality scientific publications, conference presentations, open-source algorithms, and collaborations with academic, governmental, and industry partners. You will collaborate closely with researchers from KNMI and directly with EBN, and you will work within a large Horizon Europe CETP consortium led by NORSAR. You will join an interdisciplinary research environment, working closely with authorities in seismology, geophysics, signal processing, and geospatial data science. As part of a dynamic team of researchers, Doctoral candidates, and external collaborators, you will contribute to the development of next-generation monitoring solutions that bridge emerging sensing technologies and established seismological practices. The project offers opportunities to collaborate with leading national and international partners and to contribute to teaching and supervision activities related to geophysical monitoring and data integration. Your profile A (soon-to-be-obtained) PhD in seismology, DAS, deep learning related to Earth sciences, or a related field Good command of spoken and written English Ability to deal with and process seismological time series datasets and computer programming, e.g., with Python, MATLAB, C and/or Fortran Working with a major machine-learning framework (e.g., PyTorch, TensorFlow, JAX). Familiarity with inverse theory is desirable but not essential Curiosity in numerical methods for partial differential equations, big data management, and dealing with supercomputing facilities A socially engaged, independent and creative researcher with good conceptual, communication, problem-solving and organizational skills, and an interest in engaging in team-based collaborations Our offer An inspiring multidisciplinary, international research and academic environment. The university offers a dynamic ecosystem with enthusiastic colleagues in which internationalization is an important part of the strategic agenda A full-time position for 1 year with the possibility to extend it for another year, depending on the availability of funds. Gross monthly salary between € 4,251.00 and € 5,760.00 per month, depending on experience and qualifications (UFO profile Researcher 4) A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3% Excellent support for research and facilities for professional and personal development A solid pension scheme A total of 41 holiday days per year in case of full-time employment Excellent working conditions, an exciting scientific environment, and a green and lively campus Free access to sports facilities on campus. Information and application For more information, you may contact Dr Islam Fadel (email: [email protected]). You are also invited to visit our homepage. Please submit your application before 21 November 2026, including: A motivation letter (one A4 page) outlining your motivation and fit for the position A detailed CV (including the names/information of two references, your publications, projects, and working experience) An academic transcript of BSc and MSc education, including grades, and your PhD thesis or the latest draft in English (or if your PhD thesis is not in English, a 5-page summary of your thesis in English) Applications that do not include all three will not be considered. Short-listed candidates will be invited for interviews, which will probably take place the first week of December. Preferred starting date of position: between January 1st and May 1st, 2027. Screening is part of the selection procedure. For questions about working and living in the Netherlands, please consult the official website of the Dutch Government or the Expat Center East Netherlands website. About The Department The Department of Geosciences combines Earth science knowledge with dynamic modelling and advanced remote sensing to analyse Earth systems and processes across space and time. Our goal is to contribute to global challenges concerning future demands for Earth resources and to help reduce disaster risk and the impact of natural hazards on communities living in changing environments. About The Organisation The Faculty of Geo-Information Science and Earth Observation (ITC) provides international postgraduate education, research and project services in the field of geo-information science and earth observation. Our mission is capacity development, where we apply, share and facilitate the effective use of geo-information and earth observation knowledge and tools for tackling global wicked problems. Our purpose is to enable our many partners around the world to track and trace the impact – and the shifting causes and frontiers – of today’s global challenges. Our vision is of a world in which researchers, educators, and students collaborate across disciplinary and geographic divides with governmental and non-governmental organisations, institutes, businesses, and local populations to surmount today’s complex global challenges and to contribute to sustainable, fair, and digital societies.

Core Responsibilities

Develop methods to integrate In-Well Distributed Acoustic Sensing data with offshore seismological station observations for near-real-time seismic event detection, localization, and characterization. Apply signal processing, machine learning, and physics-based simulations, and disseminate results through publications, presentations, open-source algorithms, and research collaborations.

Requirements

Applicants must have or be close to completing a PhD in seismology, Distributed Acoustic Sensing, deep learning related to Earth sciences, or a related field, along with strong spoken and written English. They should be able to process seismological time series and program in languages such as Python, MATLAB, C, or Fortran, and have experience with a major machine-learning framework; familiarity with inverse theory is desirable.

Benefits

• Holiday Allowance
• Year-End Bonus
• Research Facilities
• Professional Development
• Personal Development
• Pension Scheme
• 41 Annual Holiday Days
• Campus Sports Facilities