Job description
In state-of-the-art railway networks a huge amount of
measurement and management data are available from many different
sources. Our aim is to make these data usable in intelligent decision
support systems by developing systematic, robust, efficient, and
real-time data mining and information fusion methods for large-scale
railway data sets.
This project focuses on the integration of data from different sources
with various characteristics: data collected using different sensors and
at different sampling rates, data at multiple temporal and spatial
scales, data collected on-line and in real time, data extracted from
databases, data available in a central location, or collected or stored
decentrally, etc. These data must be integrated to make available all
the relevant information for control, management, and maintenance of the
railway infrastructure. Information fusion and data mining methods must
be developed and include the specific characteristics of railway data.
Some types of information may not be directly available or may be
missing and/or inconsistent due to sensor failures.
To address these challenges we will combine state-of-the-art data mining
and information fusion methods and/or develop new methods with new
(possibly probabilistic) models for the dynamics and evolution of tracks
and trains, for degradation and for faults, as well as advanced fault
diagnosis and
detection methods, statistical analysis, and risk management methods.
This project is a joint project of the Delft Centre for Systems and Control and the Railway Engineering group of TU Delft.
measurement and management data are available from many different
sources. Our aim is to make these data usable in intelligent decision
support systems by developing systematic, robust, efficient, and
real-time data mining and information fusion methods for large-scale
railway data sets.
This project focuses on the integration of data from different sources
with various characteristics: data collected using different sensors and
at different sampling rates, data at multiple temporal and spatial
scales, data collected on-line and in real time, data extracted from
databases, data available in a central location, or collected or stored
decentrally, etc. These data must be integrated to make available all
the relevant information for control, management, and maintenance of the
railway infrastructure. Information fusion and data mining methods must
be developed and include the specific characteristics of railway data.
Some types of information may not be directly available or may be
missing and/or inconsistent due to sensor failures.
To address these challenges we will combine state-of-the-art data mining
and information fusion methods and/or develop new methods with new
(possibly probabilistic) models for the dynamics and evolution of tracks
and trains, for degradation and for faults, as well as advanced fault
diagnosis and
detection methods, statistical analysis, and risk management methods.
This project is a joint project of the Delft Centre for Systems and Control and the Railway Engineering group of TU Delft.
Requirements
We are looking for a candidate with a PhD degree and a strong
background in systems and control, applied mathematics, data mining,
optimisation, and/or sensor fusion, as well as a strong interest in
railway operations. Additional experience in the use of deep learning
neural networks is an asset. The candidate is expected to work on the
boundary of several research domains. A good command of the English
language is required.
background in systems and control, applied mathematics, data mining,
optimisation, and/or sensor fusion, as well as a strong interest in
railway operations. Additional experience in the use of deep learning
neural networks is an asset. The candidate is expected to work on the
boundary of several research domains. A good command of the English
language is required.
Conditions of employment
TU Delft offers an attractive benefits package, including a
flexible work week, free high-speed Internet access from home (with
contracts of two years or longer), and the option of assembling a
customised compensation and benefits package (the 'IKA'). Salary and
benefits are in accordance with the Collective Labour Agreement for
Dutch Universities.
For more information about this position, please contact Bart De Schutter, phone: +31 (0)15-2785113, e-mail: b.deschutter@tudelft.nl.
Applications should include a letter of application along with a
detailed curriculum vitae, an explanation of why the proposed research
topic interests you, a list of publications, (electronic) copies of your
three most relevant journal or conference publications, the abstract
and/or summary of your PhD thesis, your MSc course programme and the
corresponding grades, names and addresses of two to three reference
persons, and all other information that might be relevant to your
application. Please e-mail your application to Nathalie van Benthem, application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number 3ME14-39.
This position will remain open until a suitable candidate has been found.
Contract type:flexible work week, free high-speed Internet access from home (with
contracts of two years or longer), and the option of assembling a
customised compensation and benefits package (the 'IKA'). Salary and
benefits are in accordance with the Collective Labour Agreement for
Dutch Universities.
For more information about this position, please contact Bart De Schutter, phone: +31 (0)15-2785113, e-mail: b.deschutter@tudelft.nl.
Applications should include a letter of application along with a
detailed curriculum vitae, an explanation of why the proposed research
topic interests you, a list of publications, (electronic) copies of your
three most relevant journal or conference publications, the abstract
and/or summary of your PhD thesis, your MSc course programme and the
corresponding grades, names and addresses of two to three reference
persons, and all other information that might be relevant to your
application. Please e-mail your application to Nathalie van Benthem, application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number 3ME14-39.
This position will remain open until a suitable candidate has been found.
Temporary,
1 year
Organisation
Delft University of Technology Delft University of Technology (TU Delft) is a multifaceted
institution offering education and carrying out research in the
technical sciences at an internationally recognised level. Education,
research and design are strongly oriented towards applicability. TU
Delft develops technologies for future generations, focusing on
sustainability, safety and economic vitality. At TU Delft you will work
in an environment where technical sciences and society converge. TU
Delft comprises eight faculties, unique laboratories, research
institutes and schools.
institution offering education and carrying out research in the
technical sciences at an internationally recognised level. Education,
research and design are strongly oriented towards applicability. TU
Delft develops technologies for future generations, focusing on
sustainability, safety and economic vitality. At TU Delft you will work
in an environment where technical sciences and society converge. TU
Delft comprises eight faculties, unique laboratories, research
institutes and schools.
Department
Mechanical, Maritime and Materials Engineering The Delft Centre for Systems and Control (DCSC) coordinates
the education and research activities in systems and control at Delft
University of Technology. The Centre's 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, robotics and transportation systems. The group
actively participates in the Dutch Institute of Systems and Control
(DISC).
the education and research activities in systems and control at Delft
University of Technology. The Centre's 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, robotics and transportation systems. The group
actively participates in the Dutch Institute of Systems and Control
(DISC).
Additional information
Bart De Schutter+31 (0)15-2785113
b.deschutter@tudelft.nl
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