Job description
Project ContextEindhoven
University of Technology (TU/e) is an internationally renowned
technical university located in the vibrant technological heart of the
Netherlands with high tech companies such as Philips, ASML, NXP and DAF
Trucks. The TU/e has an excellent reputation in collaboration with
industry proven among other things by the fact that it is the number one
university in the world with respect to joint scientific publications
with industrial partners.
The Data Science Center Eindhoven (DSC/e, www.tue.nl/dsce/)
is the response of the TU/e to the growing volume and importance of
data. Through the DSC/e, the TU/e unites its strong research groups in
areas related to data science: computer science, mathematics, electrical
engineering, industrial engineering, innovation sciences, and
industrial design. Research at the DSC/e is multidisciplinary,
reflecting the fact that data science research requires a broad range of
expertise and skills.
The TU/e has recently started a long-term
strategic cooperation with Philips Research Eindhoven on three topics:
data science, health and lighting. As a first concrete action, 70 PhD
students are being hired for these three topics using joint funding from
the TU/e and Philips, of which 18 PhD students will work on the data
science topic. These students will together with researchers from the
TU/e and Philips form a strong research community working together on
scientific and industrial challenges.
Project Themes
The
entire project is divided into 5 themes. Within each theme there is a
specific application focus that will be addressed through intensive
collaboration from several disciplines. For each individual PhD project
we briefly mention the TU/e supervisors and their departments (M&CS =
Mathematics and Computer Science, EE = Electrical Engineering,
IE&IS = Industrial Engineering and Innovation Sciences). PhD
students will have their home base at the department of the first
mentioned professor.
Data Driven Value Proposition Digital
components are being added to Philips lifestyle products. The data from
these products as well as from Philips touch points must be combined to
optimize user experience and maintain customer satisfaction. This will
be delivered through personalized e-coaching and guidance apps. We are
looking for students with expertise in one or more of the following
fields: data mining, machine learning, process analytics, predictive
analytics, and psychology. This theme has openings for 4 PhD students:
Smart Maintenance Philips has strong leadership positions in healthcare
imaging and patient monitoring systems. In the healthcare domain,
reducing equipment downtime and cost of ownership for hospitals is of
vital importance. Smart maintenance exploits that professional equipment
is connected to the internet and aims to use event and sensor data for
overall cost reduction. We are looking for students with expertise in
one or more of the following fields: operations research, maintenance,
process mining, data mining, machine learning, process analytics and
predictive analytics. This theme has openings for 3 PhD students:
Healthcare Workflows The delivery of patient care in hospital is a
complex workflow based on fixed protocols. Optimization of patient care
at reduced cost requires the orchestration of multiple clinical
workflows. Timely getting the imaging/lab tests done and getting the
results back to physicians can help quickly diagnose/treat the patient,
and save lives. The rapid digitization of diagnostics in radiology and
pathology calls for a data-driven optimization of the workflows. We are
looking for students with expertise in one or more of the following
fields: visualisation, data mining, machine learning, process analytics
and predictive analytics. This theme has openings for 4 PhD students:
develop a cost-effective system for empowering people to self-manage
their health. An important enabler is the wearable sensor technology
developed by Philips because that provides unobtrusive monitoring of
behaviour and habits. We are looking for students with expertise in one
or more of the following fields: signal processing, statistics, data
mining, machine learning, and psychology. This theme has openings for 5
PhD students:
Analytics for Lighting Data gathered from large hardware systems offers
new exciting service propositions. Philips has a unique position with
the ability to extend the hardware to collect data or to respond to the
outcome of predictive filters. In the field of building management and
connected lighting infrastructures, this includes the ability to launch
service businesses, to optimally predict cost-benefits in service
offerings, to provide adaptive configuration, and to support network
maintenance. We are looking for students with a background in one or
more of the following fields: distributed systems, real-time systems,
resource management data mining, and machine learning. This theme has
openings for 2 PhD students:
University of Technology (TU/e) is an internationally renowned
technical university located in the vibrant technological heart of the
Netherlands with high tech companies such as Philips, ASML, NXP and DAF
Trucks. The TU/e has an excellent reputation in collaboration with
industry proven among other things by the fact that it is the number one
university in the world with respect to joint scientific publications
with industrial partners.
The Data Science Center Eindhoven (DSC/e, www.tue.nl/dsce/)
is the response of the TU/e to the growing volume and importance of
data. Through the DSC/e, the TU/e unites its strong research groups in
areas related to data science: computer science, mathematics, electrical
engineering, industrial engineering, innovation sciences, and
industrial design. Research at the DSC/e is multidisciplinary,
reflecting the fact that data science research requires a broad range of
expertise and skills.
The TU/e has recently started a long-term
strategic cooperation with Philips Research Eindhoven on three topics:
data science, health and lighting. As a first concrete action, 70 PhD
students are being hired for these three topics using joint funding from
the TU/e and Philips, of which 18 PhD students will work on the data
science topic. These students will together with researchers from the
TU/e and Philips form a strong research community working together on
scientific and industrial challenges.
Project Themes
The
entire project is divided into 5 themes. Within each theme there is a
specific application focus that will be addressed through intensive
collaboration from several disciplines. For each individual PhD project
we briefly mention the TU/e supervisors and their departments (M&CS =
Mathematics and Computer Science, EE = Electrical Engineering,
IE&IS = Industrial Engineering and Innovation Sciences). PhD
students will have their home base at the department of the first
mentioned professor.
Data Driven Value Proposition Digital
components are being added to Philips lifestyle products. The data from
these products as well as from Philips touch points must be combined to
optimize user experience and maintain customer satisfaction. This will
be delivered through personalized e-coaching and guidance apps. We are
looking for students with expertise in one or more of the following
fields: data mining, machine learning, process analytics, predictive
analytics, and psychology. This theme has openings for 4 PhD students:
- User-centric
Consumer Data Analytics: User Behaviour Modelling (supervisors:
Professors De Bra (M&CS) and Petkovic (M&CS)) - User-centric
Consumer Data Analytics: User Guidance Principles Modelling
(supervisors: Professors IJsselsteijn (IE&IS) and De Bra (M&CS)) - Product-centric
Consumer Data Analytics: Product Usage Lifecycle Analysis (supervisors:
Professors Van der Aalst (M&CS) and Snijders (IE&IS)) - Product-centric
Consumer Data Analytics: Text Mining for Consumer Insight Analytics
(supervisors: Professors Snijders (IE&IS) and De Bra (IE&IS))
Smart Maintenance Philips has strong leadership positions in healthcare
imaging and patient monitoring systems. In the healthcare domain,
reducing equipment downtime and cost of ownership for hospitals is of
vital importance. Smart maintenance exploits that professional equipment
is connected to the internet and aims to use event and sensor data for
overall cost reduction. We are looking for students with expertise in
one or more of the following fields: operations research, maintenance,
process mining, data mining, machine learning, process analytics and
predictive analytics. This theme has openings for 3 PhD students:
- Transforming Event Data into Predictive Models (Professors Van der Aalst (M&CS) and Van Houtum (IE&IS))
- Turning Outcomes of Predictive Models into Better Maintenance Decisions (Professors Boxma (M&CS) and Van Houtum (IE&IS))
- Maintenance Concepts for Healthcare Systems (Professors Van Houtum (IE&IS) and Van Leeuwaarden (M&CS))
Healthcare Workflows The delivery of patient care in hospital is a
complex workflow based on fixed protocols. Optimization of patient care
at reduced cost requires the orchestration of multiple clinical
workflows. Timely getting the imaging/lab tests done and getting the
results back to physicians can help quickly diagnose/treat the patient,
and save lives. The rapid digitization of diagnostics in radiology and
pathology calls for a data-driven optimization of the workflows. We are
looking for students with expertise in one or more of the following
fields: visualisation, data mining, machine learning, process analytics
and predictive analytics. This theme has openings for 4 PhD students:
- Predictive Analytics for Healthcare Workflows (Professors Van der Aalst (M&CS) and Van Wijk (M&CS))
- Visual Analytics for Healthcare Workflows (Professors Van Wijk (M&CS) and Van der Aalst (M&CS))
- Radiology Workflow Optimization and Orchestration (Professors Van der Aalst (M&CS) and Van Wijk (M&CS))
- Modelling
and Optimization of Pathology Workflows and Flexible Semantic
Orchestration (Professors Van Wijk (M&CS) and Van der Aalst
(M&CS))
develop a cost-effective system for empowering people to self-manage
their health. An important enabler is the wearable sensor technology
developed by Philips because that provides unobtrusive monitoring of
behaviour and habits. We are looking for students with expertise in one
or more of the following fields: signal processing, statistics, data
mining, machine learning, and psychology. This theme has openings for 5
PhD students:
- Effective Self-Management by E-Coaching (Professors IJsselsteijn (IE&IS) and De Bra (M&CS))
- Unobtrusive Health and Behaviour Monitoring (Professors Van der Hofstad (M&CS) and Aarts (EE))
- Cardiac Arrhythmia and Self-Management (Professors Aarts (EE) & Van der Hofstad (M&CS))
- Blood Pressure Monitoring and Lifestyle Interventions (Professors Korsten (EE) and IJsselsteijn (IE&IS))
- Early Stratification of Cardio-vascular Health Risks (Professors Korsten (EE) & Kaymak (IE&IS))
Analytics for Lighting Data gathered from large hardware systems offers
new exciting service propositions. Philips has a unique position with
the ability to extend the hardware to collect data or to respond to the
outcome of predictive filters. In the field of building management and
connected lighting infrastructures, this includes the ability to launch
service businesses, to optimally predict cost-benefits in service
offerings, to provide adaptive configuration, and to support network
maintenance. We are looking for students with a background in one or
more of the following fields: distributed systems, real-time systems,
resource management data mining, and machine learning. This theme has
openings for 2 PhD students:
- Data Analytics for Infrastructures of Connected Lighting (Professors Lukkien (M&CS) and Linnartz (EE)
- Data Analytics for Building Management Services (Professors Linnartz (EE) and Lukkien (M&CS)
Requirements
Job qualifications
Candidates should:
have an MSc in Mathematics, Statistics, Computer Science, Psychology, Electrical Engineering or a related discipline
have a strong interest in data science research
be highly motivated, be rigorous and disciplined when developing algorithms and software according to high quality standards
be a fast learner, autonomous and creative, show dedication and be hard working
possess good communication capabilities and be an efficient team worker
be fluent in English, both spoken and written
PhD students are expected to:
perform scientific research in the domain described
collaborate with other researchers in this project
present results at (international) conferences
publish results in scientific journals
participate in activities of the group and department, at both sites
assist in teaching undergraduate/graduate courses
participate in EIT doctoral training on entrepreneurship and related topics
be willing to work at two locations (TU/e campus and Philips High Tech Campus)
Candidates should:
PhD students are expected to:
Conditions of employment
Appointment and salary
PhD students will be officially
appointed at the department of the first mentioned professor, who will
act as prime PhD supervisor.
We offer:
a full-time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months
a gross salary of € 2,083 per month in the first year increasing up to € 2,664 per month in the fourth year
a holiday allowance of 8% and an end-of-year bonus of 8.3% (annually)
assistance in finding accommodation (for foreign employees)
the
opportunity to perform research in a large-scale joint project from a
leading technical university and a leading high-tech company
support
for your personal development and career planning including
participation in the EIT doctoral training, courses, summer schools,
conference visits, research visits to other institutes (both academic
and industrial), etc.
a broad package of fringe benefits
(including excellent technical infrastructure, child day care, savings
schemes and excellent sport facilities).
Selection procedure
Candidates must apply using the web form on this page (APPLY NOW button), providing a detailed CV and a motivation letter. The motivation letter should clearly describe the interest in the projects and also indicate one or more of the PhD projects that the candidate wishes to apply for. Selection
will be on-going in the period July 1, 2014 - November 1, 2014. This
means that suitable candidates will be interviewed and if deemed fit, be
hired immediately without waiting for applications of other candidates.
PhD students will be officially
appointed at the department of the first mentioned professor, who will
act as prime PhD supervisor.
We offer:
opportunity to perform research in a large-scale joint project from a
leading technical university and a leading high-tech company
for your personal development and career planning including
participation in the EIT doctoral training, courses, summer schools,
conference visits, research visits to other institutes (both academic
and industrial), etc.
(including excellent technical infrastructure, child day care, savings
schemes and excellent sport facilities).
Selection procedure
Candidates must apply using the web form on this page (APPLY NOW button), providing a detailed CV and a motivation letter. The motivation letter should clearly describe the interest in the projects and also indicate one or more of the PhD projects that the candidate wishes to apply for. Selection
will be on-going in the period July 1, 2014 - November 1, 2014. This
means that suitable candidates will be interviewed and if deemed fit, be
hired immediately without waiting for applications of other candidates.
Additional information
Information:Please apply by using the 'Apply now' button on top of this page.
Applications via e-mail will not be accepted.
GO TO https://www.academictransfer.com/employer/TUE/vacancy/23710/lang/en/
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