We are looking for a highly creative and motivated PhD candidate to join the Data Mining Group at Eindhoven University of Technology. The candidate will be supervised by Dr. Vlado Menkovski, PI of the project and work within the Immuno-engineering research program, which is a collaboration with the department of Biomedical Engineering.

Machine Learning models, particularly deep neural networks, have shown outstanding capabilities to deliver accurate prediction in high dimensional settings [1, 2]. However, the goal of the scientific inquiry goes beyond prediction and aims at explanations that can be integrated with the existing knowledge.

In the context of data-driven scientific discovery, there are certain unique challenges. Commonly a vast amount of domain knowledge is available that can strongly benefit the development of deep learning models particularly when data is scarce and costly to obtain.

Therefore, in this project we aim to go beyond the Supervised Learning formulation of Machine Learning to Generative Models with rich structure that can capture and express the relationship between meaningful factors and the observed data. An example of such approaches would be the many flavors of conditional Variational Autoencoders that can be used for both simulation (data generation) and parameter estimation (estimate the values of factors of interest given the observations) [3, 4]. We will also study different approaches to express the known constraints and symmetries in the data generation process, particularly drawing inspiration from developments in directions such as Physic- informed neural networks.

Being part of the Immuno-engineering program, the work will be in close collaboration with researchers in Biomedical Engineering and related fields. With-in the scope of the program, problems and goals are developed and data is collected.

This PhD position provides ideal conditions for developing yourself as a researcher in the emerging field of Scientific Machine Learning. The position is embedded in a strong and growing Machine Learning community in the Mathematics and Computer Science Department. It is part of a project working on real-world and highly impactful problems in Immuno-engineering that also provides access to leading researchers in that field. As such we aim to both publish and advance the state of the art in leading Machine Learning as well as Immuno-engineering and Biomedical engineering venues.

References:

1. Corbetta, A., Menkovski, V., Benzi, R., Toschi, F. "Deep learning velocity signals allows to quantify turbulence intensity." Science Advances, eaba7281 (2021).

2. Matos, F., V. Menkovski, F. Felici, A. Pau, F. Jenko, TCV Team, and EUROfusion MST1 Team. "Classification of tokamak plasma confinement states with convolutional recurrent neural networks." Nuclear Fusion 60, no. 3 (2020): 036022.

3. Perez Rey, L. A., Menkovski, V., & Portegies, J. W. Diffusion Variational Autoencoders. IJCAI., Yokohama, Japan (2020).

4. Wang, Liwei, Yu-Chin Chan, Faez Ahmed, Zhao Liu, Ping Zhu, and Wei Chen. "Deep generative modeling for mechanistic-based learning and design of metamaterial systems." Computer Methods in Applied Mechanics and Engineering 372 (2020): 113377. 113377

We are looking for a motivated candidate with:

• A Master of Science degree in Computer Science (or similar)
• Advanced knowledge of machine learning techniques
• Strong mathematical and analytical skills
• Strong programming skills
• Strong curiosity and affinity for natural sciences
• Excellent communication skills in spoken and written English
• Creativity, free thinking, perseverance

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.