Deep neural network models of high dimensional data in a supervised learning setting can reach very high performance. Nevertheless, their applicability is still challenged by a number of drawbacks; their behavior is hard to interpret, it is hard to provide strong guarantees of generalization, it is hard to incorporate existing domain knowledge in their training process, and it is hard to apply in real contexts where data is noisy, corrupted or violating the i.i.d assumption.

Several topics related to these challenges are of specific interest for this PhD position. Specifically, we are looking for candidates with an interest in studying:

• Disentanglement of factors for high dimensional data, particularly sequential and time series
• Stability in Deep Learning, modeling highly sensitive systems (i.e. which exhibit chaotic behavior)
• Generative models for simulation of complex systems (e.g. physical processes, optimal control)
• Geometric DL models of non-Euclidian data (e.g. graphs and manifolds)
• Deep Metric Learning for capturing expert knowledge via psychometric measurements
• Reliable machine learning in the presence of noisy, missing or corrupted data.

As part of your application portfolio, you are highly encouraged to submit a research statement on one of the listed topics or a topic that you believe will address the challenges stated above.

This position is funded by the MADEin4 European project targetting the design and validation of data driven methods and tools for metrology in semiconductor industry. The project is joined by ca. 40 industrial and academic partners from Europe.

The successful candidates are expected to:

• perform scientific research in the domain described
• publish results at (international) conferences
• collaborate with other group and faculty members
• collaborate with selected MADEin4 project partners, attend project meetings and report on results
• assist with educational tasks (e.g. supervise Master students and internships)

• You are a talented and enthusiastic young researcher.
• You have experience with or a strong background in machine learning, statistics or stochastics. Preferably you finished a Master in Computer Science, (Applied) Mathematics, , (Applied) Physics, Information Technologies, Electrical Engineering or Mechanical Engineering.
• You have good programming skills and experience (Python is an asset).
• You have good communicative skills and are eager to work as part of a research team.
• You are creative and ambitious, hard-working and persistent.
• You have good command of the English language (knowledge of Dutch is not required).

•  A fulltime appointment for a period of 4 years at Eindhoven University of Technology (TU/e). with an intermediate evaluation after nine months;
• Gross monthly salary from € 2.325 (first year) to € 2.972 per month (last year);
•  A yearly holiday allowance of 8% as well as 8.3% end of the year allowance;
•  An attractive package of fringe benefits, including holiday and end-of-year allowance, a personal development program for PhD students (PROOF program), and excellent sport facilities.