PhD Position (f_m_x) - Physics-based machine learning for optimal experimental design

TN Germany; Potsdam; Vor Ort; EUR 40.000 - 60.000; Teilzeit

Zusammenfassung

An established industry player is offering a unique PhD position focused on physics-based machine learning for optimal experimental design in geothermal operations. This exciting role involves developing AI systems to tackle challenges in induced seismicity, contributing to cutting-edge research with significant real-world implications. You'll work in a dynamic, international environment, utilizing state-of-the-art equipment while receiving extensive training and career guidance. If you're passionate about merging geoscience and AI, this opportunity is perfect for you.

Leistungen

Flexible working hours

Extensive training opportunities

Career guidance

On-site day-care center

Public service benefits

Qualifikationen

• Master's degree or equivalent in relevant fields required.
• Strong programming skills in Python and machine learning experience.

Aufgaben

• Develop reliable machine learning models for THM processes.
• Generate predictive maps for evaluating induced seismicity potential.

Kenntnisse

Python

Machine Learning

Research Interest in Induced Seismicity

English Proficiency

Ausbildung

Master's degree in Mathematics

Master's degree in Computer Science

Master's degree in Geophysics

Jobbeschreibung

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PhD Position (f_m_x) - Physics-based machine learning for optimal experimental design, Potsdam

Job Details

• Location: Potsdam, Germany
• Job Category: Other
• EU work permit required: Yes
• Job Reference: 36996012b979
• Job Views: 1
• Posted: 09.05.2025
• Expiry Date: 23.06.2025

Job Description

Reference Number: 9626

Exploring optimal experimental design utilizing physics-based machine learning for geothermal operations

Are you seeking a PhD project at the interface between geoscience, machine learning, and mathematics, with an application to the highly relevant topic of induced seismicity? Then a position in this project might be appealing to you. The aim of the project is the further development and optimization of hybrid artificial intelligence systems, focusing on sparse data applications and the reliable incorporation of physical knowledge to produce explainable models. The project emphasizes the use of AI methods in sensitivity analysis, uncertainty quantification, and optimal experimental design. The topic of induced seismicity is crucial for advancing geothermal energy technologies, highlighting the social relevance of this research.

Note: The PhD position is subject to approval by third-party funders.

Your Responsibilities

1. Development of new strategies to generate reliable machine learning models for simulating thermo-hydro-mechanical (THM) processes
2. Generation of predictive maps to evaluate the potential of induced seismicity
3. Further development of the code basis
4. Presentation of results at conferences and publication in peer-reviewed journals

Your Qualifications

1. Master's degree or equivalent in mathematics, computer science, geophysics, or related fields
2. Interest in research and applications related to induced seismicity
3. Programming skills in Python and experience in machine learning
4. Proficiency in English

What We Offer

1. Ambitious and varied tasks within a dynamic, international research environment
2. State-of-the-art equipment
3. Public service benefits
4. Extensive training opportunities
5. Career guidance from our in-house Career-Centre
6. Flexible working hours and conditions
7. Support for work-life balance via benefit@work
8. On-site institute day-care center
9. Work at the Albert Einstein Science Park on Telegrafenberg in Potsdam
10. Proximity to Potsdam main train station and shuttle bus options

Additional Details

• Start date: 01 November 2024
• Duration: 36 months
• Salary: Salary group 13 according to TVöD Bund (Tarifgebiet Ost), based on qualifications
• Working hours: Part-time, 75% (approx. 29.25 hours/week)
• Workplace: Potsdam

Application Process

If interested, please submit your application by 15 August 2024 via our online form. Required documents include:

• Curriculum Vitae
• Academic credentials (transcripts and degree certificates)
• Summary of your Master thesis or equivalent
• Contact details of at least two referees
• A cover letter outlining your background and interest in the position