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PhD Researcher in Uncertainty-Aware Deep Learning for Parametric PDEs Modelling Tumor Growth
hace 2 meses
Research Topic: Uncertainty-aware deep learning for parametric PDEs modelling tumor growth.
Host Institution: Tecnalia Research Innovation, Spain.
PhD Enrolment: 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 Tasks:
- To gauge the impact of different methodologies to incorporate physics- / domain-expertise on the epistemic uncertainty of neural networks.
- To align the knowledge modeled by a neural network to explanations verified or produced by experts.
- To reduce the uncertainty propagated to the output of a network by devising automatic data augmentation methodologies that are driven by the analysis of the quantified uncertainty.
- To showcase the above goals in synthetic and real-world modeling tasks: road traffic characterization and/or tumor growth.
Expected Outcomes:
- Physics-aware neural network algorithm with quantifiable uncertainty propagation.
- Active learning strategies that leverage expert knowledge to reduce the uncertainty in the proposed neural network model.
- Methods to align the knowledge of the neural network with external expert annotation.
Eligibility Criteria:
- Mobility: 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 considered.
- The candidate must be, on the date of recruitment, a doctoral candidate (i.e., not already in possession of a 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.
Contract: Full-time contract.
Duration: 36 months, including 2 secondments at other consortium members' premises.
Salary: The gross salary amounts to €28,000 (estimated) per year.
Additional Benefits:
- Mobility allowance of €600 euro per month.
- Family allowance of €660 per month and a special needs allowance.
Evaluation Criteria:
- Step 1: Academic performance during undergraduate studies - 30 points.
- Research experience in the topic 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.
Application Process:
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 the relevant contact person.
Preferred Skills:
- Bachelor's degree in computer science, Engineering, Physics, Mathematics, or any other related fields.
- Studies that grant access to a Ph.D. Program by September 1st, 2024.
- Experience in Machine Learning and Data Science.
- Previous experience programming in scientific computing.
- Excellent command of English, together with good academic writing and presentation skills.
- Knowledge of Spanish is advisable to foster interaction with other lab mates, but not mandatory.
Preferred Qualifications:
- MSc in Artificial Intelligence, Data Science, Machine Learning, or related topics will be very positively welcomed.
- Familiar with neural networks and deep learning algorithms.
- Experience with numerical methods for partial differential equations.
- Notions on uncertainty estimation (e.g. Bayesian ML, evidential DL, Monte Carlo dropout) and Partial Differential Equations (PDE, ODE) are warmly welcomed.
- Programming skills: Python, Tensorflow, Pytorch, Scikit-learn, Latex.
- Linux at user level.
- Social skills: open-minded, resilient to criticism, objective-driven, social, team-work-oriented.
- Able to work independently and flexibly, taking initiative where required.
