Interested in joining a team on Data-Driven Scientific Computing? Come help us develop mathematical and algorithmic foundations of learning problems arising in the field of 3D printing!
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This project is concerned with the mathematical and algorithmic foundations of learning problems arising in the field of 3D printing.
In that field, a particularly relevant problem is the one of finding the right instructions to give to a 3D printer for a given design. This task is challenging, and it is currently done by trial and error. The goal of the PhD is to build, and analyze algorithms that automate this procedure. This will contribute to produce a final product with minimum waste, improved efficiency, and sustainability of production.
Mathematically, we will view the task as a learning problem: assuming that we have a database of available designs with their corresponding successful printing sequences, we want to construct an algorithm that automatically gives an admissible sequence of printing actions for a geometry that is not in the database. Building such a tool requires combining different mathematical fields such as optimization, machine learning, inverse problems, shape analysis. Having a good physical model of the mechanics of 3D printing and its numerical discretization comes also into the picture.
The candidate will work within the new research group on Data-Driven Scientific Computing led by Olga Mula, located at CASA, the Center for Analysis, Scientific Computing and Applications of TU Eindhoven. The work will take place in collaboration with researchers from the Mechanical Engineering and Built Environment department from TU/e, as part of an EAISI Exploratory Multidisciplinary AI Research project. EAISI is the Eindhoven Artificial Intelligence Systems Institute which brings together all AI activities of the TU/e. In this project, two PhD candidates will work together. The second PhD candidate will provide data from real experiments and expertise in mechanical modeling.
Eindhoven University of Technology (TU/e)
- MSc degree in Applied Mathematics (Numerical Analysis, Scientific Computing or Applied Analysis), Statistics, or Machine Learning.
- Interest and knowledge in at least two of the following topics: Scientific Machine Learning, Neural Networks, Inverse Problems, Data-Assimilation, Optimization and Numerical Optimal Control.
- Provable coding experience in Python, Julia, or C++.
- Interest in teaching activities.
- Strong interpersonal, organizational and communication skills.
- Ability to work both independently and in a team.
- Working and teaching are in English. Excellent skills in this language are required.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
- Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale 27.
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.