PhD: AI Tomography for Decarbonization via Rod Vibrations

ETH Zürich; Zürich; Vor Ort; CHF 60.000 - 72.000; Vollzeit

Benefits dieser Stelle

Competitive salary

Network-wide training events

International secondments

Zusammenfassung

ETH Zürich's COMBINE Doctoral Training Network seeks a PhD researcher to work on high-resolution measurements of vibrations in rods and rod bundles using gamma tomography. You will contribute to experimental setup, data collection and AI-assisted reconstruction in a dynamic, international research environment.

The project combines advanced numerical modelling, machine learning and tomographic techniques to advance our understanding of vibrations.

Qualifikationen

• Master's degree in engineering, physics, applied mathematics, computer science, or closely related field.
• Strong background in mathematics and machine learning.
• Programming proficiency in Python, C++ or similar.
• Experience with data processing and model interpretation.
• Ability to work independently and collaboratively in an international research environment.
• Good written and spoken English.
• Hands-on experience with experimental setup and basic electronics understanding is a plus.

Aufgaben

• Construct a setup with a single rod and a rod bundle.
• Measurements of vibrating rod/rod-bundle using gamma-tomography system.
• AI-assisted reconstruction and imaging of vibrating rods/bundles using sensors and tomography data.
• Use of machine-learning for noise reduction and data enhancement.

Kenntnisse

Mathematics

Machine learning

Python

C++

Data processing

Model interpretation

English proficiency

Electronics basics

Independent work

Ausbildung

Master's degree in engineering, physics, applied mathematics, computer science, or closely related field

Tools

Gamma tomography system

Jobbeschreibung

ETH Zürich's COMBINE Doctoral Training Network seeks a PhD researcher to work on high-resolution measurements of vibrations in rods and rod bundles using gamma tomography. You will contribute to experimental setup, data collection and AI-assisted reconstruction in a dynamic, international research environment.

The project combines advanced numerical modelling, machine learning and tomographic techniques to advance our understanding of vibrations.