PhD | Deep learning for 3D porous media reconstruction of clay and cement-based materials

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European Commission › EURAXESS › Jobs & Funding › PhD | Deep learning for 3D porous media reconstruction of clay and cement-based materials
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22/12/2021
PhD | Deep learning for 3D porous media reconstruction of clay and cement-based materials
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PhD | Deep learning for 3D porous media reconstruction of clay and cement-based materials
Application Deadline: 23/03/2022 23:00 - Europe/Brussels
Contact Details

Where to send your application.

Company
The Belgian Nuclear Research Centre

• 
  WEBSITE
  https://www.sckcen.be/en/sck-cen-academy/phd-theses-post-docs/phd-theses
  

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• 
  ORGANISATION/COMPANY
  The Belgian Nuclear Research Centre
  
• 
  DEPARTMENT
  SCK•CEN Academy for Nuclear Science and Technology
  
• 
  ORGANISATION TYPE
  Private with public mission
  
• 
  WEBSITE
  https://www.sckcen.be/en/academy
  
• 
  E-MAIL
  [email protected]
  

• 
  COUNTRY
  Belgium
  
• 
  CITY
  Mol
  
• 
  POSTAL CODE
  2400
  
• 
  STREET
  Boeretang 200
  

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  ORGANISATION/COMPANY
  The Belgian Nuclear Research Centre
  
• 
  RESEARCH FIELD
  Computer science › Informatics
  Geosciences › Geology
  Mathematics
  Physics
  
• 
  RESEARCHER PROFILE
  First Stage Researcher (R1)
  
• 
  APPLICATION DEADLINE
  23/03/2022 23:00 - Europe/Brussels
  

• 
  LOCATION
  Belgium › Mol
  
• 
  TYPE OF CONTRACT
  Temporary
  
• 
  JOB STATUS
  Full-time
  
• 
  HOURS PER WEEK
  40
  
• 
  OFFER STARTING DATE
  01/10/2022
  

OFFER DESCRIPTION

Heterogeneities at the pore scale influence flow and reactive transport processes in porous media such as natural (clay) and engineered (cement-based) barriers, both highly relevant to radioactive waste disposal. Flow properties of porous media are controlled by the geometry of their pore space (two-phase system, i.e. solid or pore), while reactive transport properties additionally depend on the spatial distribution of different minerals within the solid matrix (multiphase system) and their specific characteristics. Flow and reactive transport modelling in porous media therefore requires detailed 3D information about the pore’s structure. Yet 3D imaging techniques are cumbersome and expensive while for some porous media such as clay, the required resolution can only be achieved by 2D imaging. That is why 2D-to-3D reconstruction of porous media, that is, the process of stochastically generating 3D image realizations that are statistically consistent with a given set of 2D images or slices, has been an intensive field of research for more than a decade. Relatively recently, deep learning (DL) has been shown to be a promising tool for 2D-to-2D and 3D-to-3D reconstruction of porous media from training images while the booming development of DL for texture synthesis in general makes it a good candidate for handling challenging 2D-to-3D reconstructions. Yet so far DL has only been used for 2D-to-3D reconstruction of binary subsurface rocks and geologic layers and its potential to deal with more complex heterogeneous porous media remains unexplored. This PhD focusses on developing a DL-based 2D-to-3D reconstruction approach suitable for clay and other complex porous materials relevant to radioactive waste disposal.

More Information
Additional Information
Benefits

Why would you choose to perform your PhD research at SCK CEN?

Academic & research-oriented: SCK CEN ensures a close cooperation with the academic sector, combined with the unique character of our international research environment. You will work together with supportive experienced experts and use innovative facilities, specifically designed for nuclear experiments.

 

Multidisciplinary research: Your PhD is inextricably linked with other areas of expertise, either because of the versatile character of your own subject, or indirectly thanks to knowledge exchanges with colleagues.

 

Excellent training centre: SCK CEN is proud of its own SCK CEN Academy, which organises our own internal training courses. Obviously, you can also benefit from these opportunities and the Academy’s excellent reputation.

Eligibility criteria

See https://www.sckcen.be/en/sck-cen-academy/phd-theses-post-docs/phd-theses...

Selection process

See https://www.sckcen.be/en/sck-cen-academy/phd-theses-post-docs/phd-theses...

Web site for additional job details

https://www.sckcen.be/en/sck-cen-academy/thesis-and-internship-topics/deep-le...

Requirements
Offer Requirements
Skills/Qualifications

The minimum diploma level of the candidate needs to be

• Master of sciences in engineering
• Master of sciences

Specific Requirements

The candidate needs to have a background in

• Mathematics
• Informatics
• Geology
• Physics

Estimated duration

4 years

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Work location(s)
1 position(s) available at
SCK CEN
Belgium
Antwerp
Mol
2400
Boeretang 200

EURAXESS offer ID: 722334

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