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Research Associate for the Project “Prediction of Seismic Wavefields at Depth ” in the Project "Newtonian Noise Cancelling Headphones"

Universität Hamburg -
Hamburg

Jetzt bewerben

Details zur Stelle

Leistungen

Flexible Arbeitszeiten

Qualifikationen

MATLAB
Doktorat
Datenanalyse
Forschungserfahrung
Master
Machine Learning
Physik
Python
Unterrichten

Vollständige Stellenbeschreibung

InstitutionFaculty of Mathematics, Informatics and Natural Sciences, Department of Earth System Sciences, Geophysics

Salary levelEGR. 13 TV-L

Start dateas soon as possible pending approval of external funding, fixed until 30.06.2029 (This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act [Wissenschaftszeitvertragsgesetz, WissZeitVG]).

Application deadline01.08.2026

Scope of workpart-time

Weekly hours75 % of standard work hours per week

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" in the scope of R&D for the Einstein Telescope in Germany. This project aims to develop advanced methods for characterizing and mitigating Newtonian Noise (NN) by integrating machine learning, high-performance computing, and novel sensor technologies. The successful candidate will join a dynamic research environment, contributing to the development of next-generation seismic monitoring systems and supporting the Einstein Telescope’s sensitivity goals.

Your responsibilities

Duties include academic services in the project named above. Research associates may also pursue further academic qualifications outside of their work responsibilities. They may also pursue doctoral studies outside of working duties.

Within PREDICT-NN this PhD position 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 as well as developing and implementing an appropriate machine learning architecture to predict the ground motion at depth. A key task will be the robust estimation of corresponding uncertainties. The position will collaborate with national and international partners to validate prediction capabilities against real-world data from testbeds such as the Black Forest Observatory. The research will contribute to reducing reliance on borehole sensors and optimizing approaches for NN reduction in the Einstein Telescope.

This position offers the opportunity to work at the intersection of geophysics, machine learning, and gravitational wave science, directly contributing to the next-generation gravitational wave detectors.

Your profile

A university degree in a relevant field.

We welcome applications from candidates who meet the following requirements:

a 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
an outstanding academic track record with demonstrated research potential in machine learning applications and seismological data analysis
a strong dedication to scientific inquiry, reliability, and the ability to collaborate effectively in interdisciplinary teams

Essential Skills and Qualifications:

a strong background in seismological data analysis and/or seismic site characterization
experience in machine learning, particularly in the application to seismological problems
programming proficiency in Python, MATLAB, or a comparable language for scientific computing
an 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:

experience with
- 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

Outstanding candidates will bring a high intrinsic motivation, individual responsibility, creative scientific thinking, and extensive social and team skills.

We offer

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

Universität Hamburg—University of Excellence is one of the strongest research educational institutions in Germany. Our work in research, teaching, educational and knowledge exchange activities is fostering the next generation of responsible global citizens ready to tackle the global challenges facing us. Our guiding principle “Innovating and Cooperating for a Sustainable Future in a digital age” drives collaboration with academic and nonacademic partner institutions in the Hamburg Metropolitan Region and around the world. We would like to invite you to be part of our community to work with us in creating sustainable and digital change for a dynamic and pluralist society.

The University of Hamburg is committed to equity. Diversity enriches our university life, whether in our studies, research, teaching, education, or workplace. 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.

Instructions for applying

Contact

Prof. Dr. Conny Hammer

[email protected]

+49 40 2395-25050

Reference number

151

Location

Bundesstraße 55
20146 Hamburg

Application deadline

01.08.2026

Use only the online application form to submit your application with the following documents:

a cover letter in which you describe your motivation and qualifications for the position
a CV including relevant competences, skills and publication list, if applicable
copies of degree certificate(s) and transcripts of records for previous studies (Bachelor and/or Master); please indicate expected date of graduation if your Master’s degree is not completed
contact information of two references

If you experience technical problems, send an email to [email protected].

apply

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