position at TECNALIA for the IN-DEEP European Doctoral Network Real-time inversion using self-explainable Deep Learning driven by expert knowledge

hace 7 días

Elexalde Derio, España TECNALIA DOCTORADOS A tiempo completo

Reference number: DC3

PhD research focus: Uncertainty-aware deep learning for parametric PDEs modeling tumor growth.

Host institution: Tecnalia Research & Innovation, Spain

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

Main Supervisor: Prof. Dr. Javier del Ser, Fundación Tecnalia Research & Innovation (Tecnalia), Derio, Spain,

Co-supervisor: Prof. Dr. Alessandro Reali Universita Degli Studi Di Pavia (UniPV), Pavia, Italy,

Scientific duties

Overall research focus: Uncertainty, Explainability and Active Learning methodologies for Physics-/Domain-expertise Driven Neural Networks

  • To measure the impact of different approaches to incorporate physics-/domain-expertise on the epistemic uncertainty of neural networks.
  • To align the knowledge represented by a neural network with explanations validated or produced by experts (e.g., manual identification of key areas in an input image).
  • To minimize the uncertainty transferred to the network's output by creating automatic data enhancement techniques guided by the analysis of the quantified uncertainty in goal 1.
  • To demonstrate the above objectives in both simulated and real-world modeling scenarios: characterization of road traffic and/or tumor growth.

For further details about the IN-DEEP Doctoral Network, kindly visit the program's website.

Expected results

  1. Physics-informed neural network algorithm with measurable uncertainty propagation.
  2. Active learning methodologies utilizing expert knowledge to decrease uncertainty in the proposed neural network model.
  3. Approaches to harmonize the neural network's knowledge with external expert annotations.
  4. Application of the proposed approach to parametric models of tumor growth and/or road traffic models.
  5. 2+ peer-reviewed articles.
  6. Participation in 2+ relevant international conferences.

Eligibility Requirements

  • Mobility: At the time of selection, the applicant should not have lived or conducted their primary activities (work, studies, etc.) in Spain for more than 12 months within the previous 36 months. Time spent undergoing procedures for obtaining refugee status under the Geneva Convention or mandatory national service will not be counted.
  • The candidate should be, upon selection, a Ph.D. candidate (i.e., not yet awarded a Ph.D. degree). Researchers who have defended their doctoral dissertation but have not received the formal degree will not be considered eligible.
  • The candidate must commit to working solely for this project.

Contract: Full-time position

Duration: 36 months, including 2 periods working at other consortium members' locations

Anticipated start date: By September 1st, 2024 (latest)

Salary: The gross annual salary is € (estimated). Additionally, the candidate will receive a monthly mobility allowance of €600, and if applicable, a monthly family allowance of €660 along with a special needs allowance.

Evaluation Criteria

  • Step 1:
    • Academic performance during undergraduate studies - 30 points.
    • Research experience relevant to the topic of the call, including publications, projects, and internships - 30 points.
    • Awards, recognitions, other major roles, and accomplishments as a student - 10 points.
    • Additional courses, certifications, training programs, continuous education - 5 points.
  • Step 2: Applicable only to candidates scoring 50 or above in step 1:
    • Interview to evaluate communication skills, initiative, critical thinking, and drive for pursuing a Ph.D. - 15 points.
    • Letters of recommendation - 10 points.

In case of tie scores, preference will be given to female candidates.

Application Process: Interested individuals should submit a CV, a concise letter of intent (up to one page), and the details of two referees who can offer recommendation letters.

Women are strongly encouraged to submit their applications

This initiative is supported by funding from the EU's Horizon Europe research and innovation program under the Marie Sklodowska-Curie GA No.

Specific Requirements

  • Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, or related fields.
  • Completion of studies enabling enrollment in a Ph.D. program by September 1st, 2024.
  • Proficiency in Machine Learning and Data Science.
  • Prior experience in scientific computing programming.
  • Language skills: Proficient in English, with strong academic writing and presentation abilities. Knowledge of Spanish is advantageous for fostering collaboration with other team members but not mandatory.

Desired Skills

  • Master's degree in Artificial Intelligence, Data Science, Machine Learning, or related disciplines is highly valued.
  • Familiarity with neural networks and deep learning algorithms.
  • Experience with numerical techniques for solving partial differential equations.
  • Familiarity with uncertainty estimation methods (e.g., Bayesian ML, evidential DL, Monte Carlo dropout) and Partial Differential Equations (PDE, ODE, ...) is a plus.
  • Proficient in programming: Python, Tensorflow, Pytorch, Scikit-learn, Latex. Basic understanding of Linux at the user level.
  • Interpersonal skills: open-minded, receptive to feedback, goal-oriented, sociable, and collaborative.
  • Capable of working autonomously and adaptably, showing initiative when necessary.

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