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Nuclear Engineering / Computational Imaging● Open★ EU-FUNDEDPreview

PhD Position: Gamma Tomography of Vibrating Rod Bundles (MSCA-COMBINE Network)

Doctoral researcher developing AI-assisted gamma-tomography techniques to measure vibrations in rod bundles at ETH Zürich / Paul Scherrer Institute.

Best for:Best for engineering or physics graduates with strong Python/C++ skills, a background in machine learning, and an interest in experimental nuclear-systems research.

ETH Zürich · Villigen, Switzerland · R1 · First-stage researcher
More professors at ETH Zürich
Gamma tomographyFluid-structure interactionVibration measurementAI-assisted image reconstructionMachine learning for noise reductionRod bundle dynamics
Deadline
N/A
Funding
Salaried
Competitive salary
Start date
—
Full-time
Location
Villigen
Switzerland

Overview

This PhD position is part of the Marie Skłodowska-Curie Doctoral Training Network COMBINE, a consortium of 17 academic institutions and 14 industrial partners tackling fluid-structure interaction challenges relevant to decarbonization in energy and process engineering. The successful candidate will develop advanced tomographic techniques to evaluate vibrations in rod bundles, leveraging a state-of-the-art, in-house high-resolution gamma tomography system at the Paul Scherrer Institute. The work spans experimental setup construction, tomographic measurements, and the application of machine-learning methods for image reconstruction and noise reduction. Researchers benefit from interdisciplinary, international, and inter-sectoral training, including secondments at leading partner institutions across Europe. Applicants should hold a master's degree in a STEM field and have strong programming and mathematical skills.

gamma tomographyvibration analysismachine learningimage reconstructionfluid-structure interactionrod bundlesMSCA doctoral networkexperimental measurements

What you'll do

  • Design and construct experimental setups containing single rods and rod bundles
  • Conduct vibration measurements on rods and rod bundles using the in-house high-resolution gamma tomography system
  • Develop and apply AI-assisted reconstruction and imaging methods that fuse sensor data with tomography data
  • Implement machine-learning algorithms for noise reduction and data enhancement
  • Participate in interdisciplinary, inter-sectoral training across the COMBINE network
  • Undertake international secondments at academic and industrial partner institutions in Europe
  • Engage in network-wide training events and local courses in technical and transferable skills
  • Collaborate with internationally recognized academic and industrial experts

Requirements

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

Degree offered: PhD

Language: English; good written and spoken proficiency required

Instruction: English

Required skills
  • Strong background in mathematics and machine learning
  • Programming proficiency in Python, C++, or similar languages
  • Experience with data processing and model interpretation
  • Ability to work independently and collaboratively in an international research environment
  • Good written and spoken English
Nice to have
Hands-on experience with experimental setup constructionBasic understanding of electronics
Eligibility
  • Must hold a master's degree in engineering, physics, applied mathematics, computer science, or a closely related field
  • Must satisfy Marie Skłodowska-Curie Actions eligibility criteria for doctoral candidates

Funding & terms

Type
Salaried
Amount
Competitive salary
Duration
—
Programme
Marie Skłodowska-Curie Doctoral Training Network COMBINE
EU-FUNDEDEuropean Union – Marie Skłodowska-Curie Actions

How to apply

Submit an online application exclusively through the ETH Zürich online application portal. Applications sent by email or postal services will not be considered. Include a CV, transcripts, and a cover letter. Questions about the position can be directed to Prof. Annalisa Manera by email, but no applications should be sent to her directly.

Documents required
  • CV
  • Transcripts
  • Cover letter
↗Apply on site

Contact

Annalisa Manera· Professor / Supervisor

maneraa@ethz.ch

https://ns-ecmfl.ethz.ch/

AM
Prof.
Annalisa Manera
Laboratory of Nuclear Systems and Multiphase Flows
Lab
Laboratory of Nuclear Systems and Multiphase Flows
ETH Zürich
Marie Skłodowska-Curie Doctoral Training Network COMBINESwitzerland
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