Researcher Engineer - Marie Curie - MOIRA Project

Siemensglobal (Leuven VBR, België) 6 dagen geleden gepost

Marie Curie Early
Stage Researcher – MOIRA – Transfer learning for end-of-line testing and
monitoring in fleets

Introduction:

Siemens Digital Industries Software, in Leuven,
Belgium will have an open PhD position in the frame of the European Training
Network on Monitoring Large-Scale Complex Systems (“MOIRA”), funded by the European Commission through the H2020
“Marie Skłodowska-Curie Innovative Training Networks” (ITN) program.


The objective of the MOIRA project is to develop the next generation of
knowledge discovery methodologies, algorithms and technologies, so enabling
data-driven, plant-wide fleet monitoring, with the focus on real-time
diagnostics and prognostics. This objective will be achieved by having 15
early-stage researchers (ESRs) in a collaborative network between top European
universities, research institutes, wind-turbine, and plant operators, OEMs and
industrial stakeholders with expertise in mechanical engineering, computer
science, signal processing, vibrations, inverse problems, operations
maintenance, data analytics, and networks.


The PhD project connected to this vacancy involves SISW as lead
beneficiary and the KU Leuven (KUL) as the academic partner and PhD awarding
entity. The ESR will become part of the research team of the SISW TEST division
and will cooperate closely with the SISW staff as well as other international
visiting researchers and students. The ESR will be enrolled as a PhD student in
the KUL doctoral school.

PhD Project Description:

The ESR will focus on novel methodologies for assessing the performance
and usage of assets in a fleet throughout the product lifecycle. A specific
application of interest is the end-of-line quality control testing of vehicles,
based on NVH (Noise, Vibration, Harshness) measurements. Machine learning and
deep learning techniques have the potential to automatically and objectively
assess the status of the vehicle based on such measurements. However, these
techniques rely on the availability of a sufficiently large training dataset,
which may be infeasible to obtain in industrial practice (the so-called data
scarcity problem). To overcome the data scarcity problem, the ESR will research
two transfer learning strategies. 


In the first strategy, a physical simulation model will provide a source dataset
that can be used for training of an initial machine learning model. Such an
approach was already successful in previous work on bearing fault detection,
where simulation models were used for training a support vector machine or a
deep neural network. However, it is expected that other cases will require more
advanced transfer learning methods (in particular, domain adaptation), for
example, if the simulations cannot capture all features which will be present in
real-life data and which are relevant for the specific industrial context. 


The
second strategy assumes that the machine-under-study is part of a larger fleet
of similar (but not necessarily identical) machines. For example, if older machines
have been in the field already for some years, the data and knowledge gained on
these machines might be transferrable to predict quantities on a newly deployed
machine.


Timeline and remuneration: The earliest
start time is 1st March 2021. The Marie Curie grant foresees funding for a
duration of 3 years, however, given that a typical PhD duration in Belgium is 4
years, extra funds are reserved such that a fourth PhD year can be added.
Furthermore, in order to stimulate intersectoral and international mobility,
the ESR will have short research visits (so-called “secondments”) to at least
two Beneficiary/Partner Organisations (with the secondments not exceeding 30%
of the duration of the doctoral training).


The remuneration is generous and will be in line with the EC rules for
Marie Curie grant holders. It consists of a salary augmented by a mobility
allowance, resulting in a net monthly salary of about 1900-2300 Euro depending
on family status.

Supervisors and main contacts:

Siemens Digital Industries Software: dr. Bram Cornelis (research
manager)

KUL: prof. Konstantinos Gryllias, prof. Wim Desmet

Candidate Profiles:

Applicants must have a MSc degree or
equivalent
in mechanical/mechatronic
or related field, which will qualify for starting a PhD programme.

They must have:

·       
Excellent
qualification in engineering disciplines such as mechanics, electronics,
physics and mathematics;

·       
Very
strong interest in machine learning

·       
Experience
with scientific computing and high-level programming languages such as Matlab
or Python.

·       
Affinity
with the scientific research methodology;

·       
Interest
to develop and implement a long-term research programme leading to a PhD;

·       
Capability
to work independently and in a team;

·       
Fluent
in spoken and written English;

Competences that are considered as an additional advantage:

·       
Previous
hands-on experience with machine learning (incl. deep learning) is a huge
asset.

·       
Solid
background in experimental vibration and/or acoustic testing and signal
processing is an advantage;

·       
Solid
background in CAE methodologies and software is an advantage;

Marie Curie eligibility
Criteria

To be eligible, you need to be an "early stage
researcher" i.e. simultaneously fulfill the following criteria at the time
of recruitment:

·       
Mobility: you must not have resided
or carried out your main activity (work, studies, etc...) in Belgium for more
than 12 months in the 3 years immediately prior to your recruitment under the
MOIRA project.

·       
Qualifications and research
experience: you must be in the first 4 years of your research career after the
master degree was awarded.



Organization: Digital Industries

Company: Siemens Industry Software NV

Experience Level: Early Professional

Job Type: Full-time

Researcher Engineer - Marie Curie - MOIRA Project

Apply On Company Site
Back to search page
;