Data driven modeling is widely used for optimizing modern digital systems, addressing our inability
to model these systems from first principles due to their complexity and the high dimensional data
they generate. However, the data driven models often contain millions of parameters, making it
difficult to understand their behavior and to trust their output. Moreover, they require large
amounts of labeled data, which is not always available. There are data driven approaches
targeting these challenges (e.g. surrogate modeling or unsupervised learning), but they are often
limited in their application. Knowledge driven approaches, on the other hand, are often
explainable and can leverage expert knowledge to deal with few labeled samples, but struggle
with noisy and high dimensional data.
We are looking for candidates that would like to explore integrating data driven with knowledge
driven modeling to provide trustworthy modeling of high dimensional data.
This position is funded by the InShape European project aiming to develop and demonstrate a
novel metal powder bed fusion process for additive manufacturing in four different industrial use
cases (aerospace, energy, space and automotive). The project has 10 industrial and academic
partners. Our focus in the project is on developing data driven methods for high-dimensional
sequential and/or image data for optical systems (laser beam shaping) and the monitoring, control
and optimization of the manufacturing process, in order to improve product quality and
sustainability of the process.
In the context of laser beam shaping, one possible application for the data driven methods will be
the phase retrieval problem, where the goal is to identify the phase-distribution that is required to
generate a specific irradiance profile (or point-spread function). In general, this inverse problem
has no exact solution. While approximate solutions exist, they tend to be computationally
expensive and/or unreliable.
The successful candidate for the Postdoc position is expected to:
- Perform scientific research on integrated data and knowledge driven modelling in general and to validate the results in the InShape project;
- Publish results at (international) conferences and journals;
- Collaborate with other group and faculty members;
- Collaborate with selected InShape project partners, attend project meetings and contribute to deliverables and project outcome;
- Assist with educational tasks (e.g. supervise(under)graduate/internships students and lab/course assignments).