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PhD on integrated data and knowledge driven modeling of high-dimensional data

PhD on integrated data and knowledge driven modeling of high-dimensional data

Position
PhD-student
Department(s)
Mathematics and Computer Science
FTE
1,0
Date off
04/09/2022
Reference number
V32.5808

Job description

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 PhD position is expected to:

  • Perform scientific research on hybrid data/knowledge driven modelling in general and to validate the results in the InShape project;
  • Publish results at (international) conferences;
  • 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 students and lab/course assignments), max 10% of the time.

Job requirements

  • You have a master degree in Computer Science, (Applied) Mathematics, Information Technologies, or a related field;
  • You have a strong background in data driven methods (e.g. machine learning, statistics, stochastics) and/or knowledge driven methods (e.g. logic programming, deductive databases, automated reasoning, expert systems).
  • You have good programming skills and experience (OCaml or Python is an asset);
  • You have good communication 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).

Conditions of employment

  • 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.

Information and application

For questions regarding this position, you can send an email to dr. Mike Holenderski (m.holenderski[at]tue.nl) with email subject “PhD position in InShape”.

For information about terms of employment, click here or contact HR Services (HRServices.MCS[at]tue.nl).

Please visit www.tue.nl/jobs and www.tue.nl/en/education/graduate-school/ to find out more about working at TU/e!

Application

We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include a:

  • Cover letter in which you describe your motivation, scientific interests and qualifications for the position (generic letters will be rejected);
  • Detailed Curriculum vitae, including a list of your publications;
  • MSc and BSc transcripts;
  • 2 Recent recommendation letters.

We look forward to your application.
We do not respond to applications that are sent to us in a different way and incomplete applications will not be considered.

Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files.

This vacancy will be listed until a suitable candidate is found. We will start selecting applications starting
September 5th, 2022.