PhD Positions Sparse Data Driven Methods for Prognosis of Electric Vehicles

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Faculty Mechanical, Maritime and Materials Engineering

The Faculty of 3mE carries out pioneering research, leading to new fundamental insights and challenging applications in the field of mechanical engineering. From large-scale energy storage, medical instruments, control technology and robotics to smart materials, nanoscale structures and autonomous ships. The foundations and results of this research are reflected in outstanding, contemporary education, inspiring students and PhD candidates to become socially engaged and responsible engineers and scientists. The faculty of 3mE is a dynamic and innovative faculty with an international scope and high-tech lab facilities. Research and education focus on the design, manufacture, application and modification of products, materials, processes and mechanical devices, contributing to the development and growth of a sustainable society, as well as prosperity and welfare.

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Functie omschrijving

The introduction of next generation heavy electric vehicles, such as electric trucks, is seen as an important contribution to worldwide efforts to curb greenhouse gases emission levels. Still, to deliver their promised performances, such novel electric vehicles should be robust to faults and be designed to optimize their maintenance.

While advanced diagnosis and prognosis algorithms that are suitable for fleets of complex vehicles are model-based, their design, tuning and validation require considerable amounts of data. Large and densely populated data sets, unfortunately, may not always be available, especially during the design phase of such vehicles. The challenge of tuning and validating diagnosis and prognosis algorithms using datasets that are sparse over time and over the vehicles’ population is precisely the motivation for the two PhD openings.

The successful candidates will carry out research as part of the project “SPARSITY: using data from sparse measurements for predictive maintenance”, which is an academic-industrial collaboration between Dr. Ferrari’s group at Delft Center for Systems and Control (TU Delft, The Netherlands) and Volvo Group, a world-leading automotive company based in Gothenburg (Sweden). Research topics will include, but will not be limited to:


  • adapting state-of-the-art system identification algorithms to use sparse datasets;

  • uncertainty quantification and propagation in complex nonlinear systems;

  • probabilistic methods for diagnosis and prognosis thresholds design and validation;

  • sensitivity analysis of diagnosis and prognosis performances with respect to data sparsity.

The resulting methodologies and algorithms will be tested against real use cases provided by Volvo, where the candidates may spend a secondment period.

The department Delft Center for Systems and Control (DCSC) of the faculty Mechanical, Maritime and Materials Engineering, coordinates the education and research activities in systems and control at Delft University of Technology. The Centers' research mission is to conduct fundamental research in systems dynamics and control, involving dynamic modelling, advanced control theory, optimisation and signal analysis. The research is motivated by advanced technology development in physical imaging systems, renewable energy, robotics and transportation systems.

Functie eisen

Applicants should have obtained a M.Sc. degree in a field related to the project, such as:


  • Electrical or Electronics engineering

  • Systems & Control

  • Applied Mathematics

  • Mechanical engineering

  • Vehicle engineering

A good command of the English language is required. Candidates with a background in fault diagnosis/prognosis, automotive electric powertrains or probabilistic methods such as Polynomial Chaos Expansion or Gaussian Process Regression are especially encouraged to apply.

Arbeidsvoorwaarden

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3061 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Informeren en solliciteren

For information about this vacancy, you can contact Riccardo Ferrari, Assistant professor, email: r.ferrari@tudelft.nl, tel: +31 (0)15 2783519.

For information about the selection procedure, please contact Irina Bruckner, HR advisor, email: application-3me@tudelft.nl.

To apply, please submit:


  • motivation letter (up to one page)

  • curriculum vitae;

  • list of publications, including citation count;

  • research statement (up to three pages);

  • transcripts of all exams taken and obtained degrees (in English);

  • names and contact information of two academic references (e.g. project/thesis supervisors);

  • up to 3 research-oriented documents (e.g. thesis, conference/journal publication)

compiled into a single pdf file by October 31st, 2020 to application-3me@tudelft.nl.

When applying for this position, please refer to vacancy number TUD00496.

Please note: Applications will not be processed if all documents required are not compiled into a single pdf document.

The position will ideally start on January 1st 2021.

A pre-employment screening can be part of the application procedure.

Acquisitie naar aanleiding van deze vacature wordt niet op prijs gesteld.

Uren:
38.0 uur per week
Type vacature:
Intern

Vaardigheden

  • Er is geen minimale opleiding vereist
  • Voor deze functie is er geen minimale werkervaring vereist
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