Ph.D. position at the University of the Basque Country (UPV/EHU, Spain) for the IN-DEEP European Doctoral Network Real-time inversion using self-explainable Deep Learning driven by expert knowledge

Reference number: DC1

PhD research topic: Optimized explainable deep learning algorithms for inverse problems. Applications in geophysics.

Host institution: University of the Basque Country (UPV/EHU), Bilbao, Spain

PhD Enrolment: University of the Basque Country (UPV/EHU), Bilbao, Spain

Main Supervisor: Dr. David Pardo, University of the Basque Country, david.pardo@ehu.eus 

Co-supervisor: Dr. Judit Muñoz-Matute, Basque Center for Applied Mathematics, Spain, jmunoz@bcamath.org

Scientific tasks:

1. To validate the performance of deep PDE solvers for parametric PDEs with traditional numerical methods. 
2. To design an explainable algorithm that guarantees the solution's uniqueness in Deep PDE solvers for inverse problems. 
3. To invert borehole resistivity measurements based on electromagnetic data.

For more information on the IN-DEEP Doctoral Network, please visit https://www.in-deep.science 

Expected outcomes:

1. A novel certified method and software for inverse problems in geophysics
2. Validation on benchmark problems with a dataset generated using Deep PDE solvers for parametric problems
3. Inversion of borehole resistivity measurements in a realistic Earth parametrization 
4. 2+ peer-reviewed publications
5. 2+ participation in relevant international conferences

Eligibility Criteria:

• Mobility: At the time of recruitment, the researcher must not have resided or carried out his/her main activity (work, studies, etc.) in Spain for more than 12 months in the 36 months immediately before the recruitment date. Time spent as part of a procedure for obtaining refugee status under the Geneva Convention or compulsory national service are not taken into account.
• The candidate must be at the date of recruitment a doctoral candidate (i.e. not already in possession of doctoral degree). Researchers who have successfully defended their doctoral thesis but who have not yet formally been awarded the doctoral degree will not be considered eligible.
• The candidate must agree to work exclusively for the action.

Specific requirements:

• Bachelor's degree in Science, Engineering, or any other related fields
• Studies that grant access to a Ph.D. Program by September 1^st, 2024. 
• Programming skills: Previous experience programming in scientific computing.
• Language: Excellent command of English, together with good academic writing and presentation skills.

Preferred skills:

• Familiar with neural networks and deep learning algorithms
• Experience with numerical methods for partial differential equations
• Able to work independently and flexibly, taking initiative where required
• Able to communicate and collaborate effectively in a team setting

Contract:Full-time contract

Duration:36 months, including 2 secondments at other consortium members’ premises

Estimated starting date: September 1^st, 2024 at the latest

Salary: The gross salary amounts to €32,098 (estimated) per year, which encompasses a mobility allowance of €600. Where applicable, the candidate will also receive a family allowance of €660 per month and a special needs allowance. 

Evaluation criteria:

• Step 1:
  • Academic performance during the undergraduate studies - 30 points
  • Research experience in the area of the call, including publications, projects, and internships - 30 points
  • Awards, honors, other significant roles and achievements as a student - 10 points
  • Additional coursework, certifications, training programs, continuous learning - 5 points
• Step 2: Only for those scoring 50 or above in step 1:
  • Interview to assess communication skills, initiative, critical thinking, and motivation to pursue a PhD. - 15 points.
  • Letters of recommendation - 10 points 

In case of candidates achieving equal scores, priority will be given to female applicants.

Deadline:March 20^th, 2024

Application: Please send an email with your CV, a letter of interest (up to one page), and the contact information of two referees who can provide letters of recommendation to david.pardo@ehu.eus.

Women are encouraged to apply!