Research Associate for the Project “Prediction of Seismic Wavefields at Depth ” in the Project [...]

Institution : Faculty of Mathematics, Informatics and Natural Sciences, Department of Earth System Sciences, Geophysics

Salary level : EGR. 13 TV-L

Start date : until (subject to external funding approval)

Application deadline :

Scope of work : part‑time (75 % of standard work hours)

Overview

The position is part of the proposed project "Prediction of Seismic Wavefields at Depth" and belongs to the joint project PREDICT‑NN "Newtonian Noise Cancelling Headphones" under the R&D for the Einstein Telescope. The successful candidate will contribute to advanced methods for characterising and mitigating Newtonian Noise by integrating machine learning, high‑performance computing, and novel sensor technologies.

Responsibilities

Duties include academic services in the project. The PhD researcher will focus on predicting the seismic wavefield at depth using surface seismic measurements and local site proxies. Responsibilities include:

• Collecting and preparing datasets from the KiK‑net observation network.
• Developing and implementing a machine learning architecture to predict ground motion at depth.
• Robustly estimating corresponding uncertainties.
• Collaborating with national and international partners to validate predictions against real‑world data from testbeds such as the Black Forest Observatory.
• Contributing to the reduction of reliance on borehole sensors and optimising approaches for Newtonian‑noise reduction in the Einstein Telescope.

Profile

Applicants should hold a university degree in a relevant field.

We welcome applications from candidates who meet the following requirements:

• Completed research‑oriented Master’s degree in Geophysics, Physics, Computer Sciences, Applied Mathematics, or a closely related field; the degree must be completed upon signing the employment contract.
• Outstanding academic track record with demonstrated research potential in machine learning applications and seismological data analysis.
• Strong dedication to scientific inquiry, reliability, and the ability to collaborate effectively in interdisciplinary teams.

Essential Skills And Qualifications

• Strong background in seismological data analysis and/or seismic site characterization.
• Experience in machine learning, particularly its application to seismological problems.
• Programming proficiency in Python, MATLAB, or a comparable language for scientific computing.
• Excellent command of English, both verbal and written, with the ability to communicate effectively within an international research environment.

Prior Experience in Several of the Following Fields Is Beneficial

• High‑performance computing or parallel processing for geophysical applications.
• Various machine‑learning architectures for geophysical datasets.
• Seismic wavefield prediction or the analysis of seismic site parameters.
• The quantification of uncertainties in machine‑learning models.
• Open‑source scientific software development or contribution.

Benefits

• Reliable remuneration based on wage agreements.
• Continuing education opportunities.
• University pensions.
• Attractive location.
• Flexible working hours.
• Work‑life balance opportunities.
• Health management (EGYM Wellpass).
• Educational leave.
• 30 days of vacation per annum.

Equal Opportunity Statement

The University of Hamburg is committed to equality, diversity and inclusion. We therefore welcome all applications, regardless of gender, gender identity, sexual orientation, ethnic or social background, age, religion or belief, disability, or chronic illness. Severely disabled and disabled applicants with the same status will receive preference over equally qualified non‑disabled applicants.