aplogies for cross postings
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*Postdoc position on Learning over distributed streaming data*
We are looking for an excellent Postdoc to work on the development of
new machine learning methods for distributed streaming data generate in
the context of the Internet of Things.
Learning over data generated in the context of IoT sets forth a number
of challenges that have to do with the nature of the data and the
processes that generate them. Most of the DM and ML algorithms assume
that the data are identically independently distributed, i.e. drawn
independently from stationary distributions. This is far from being the
standard scenario in the IoT devices; here the data generation processes
have a strong spatio-temporal dimension, which needs to be taken into
account during the data modeling process if one wants to perform
reliable knowledge extraction. Directly related to the spatial dimension
is the issue of data fusion and aggregation, which has to be done in a
manner that accommodates the spatial dimension, but also, more
generally, the redundancies and interactions that exist between the
different data sources, a typical scenario in a sensor network is the
complementary views of the same situation that the different sensors
deliver. Moreover IoT devices can regularly fail, e.g. limited battery
life-time, loss of connectivity, failure, resulting in incomplete data
availability. Any learning algorithm that is going to be used in such an
environment for model building as well as its models should be able to
cope with incomplete and missing information, in addition to coping with
concept drift and changing data distributions.
The position is funded by a European H2020 research project for three
years.
The successful candidate will join the data mining and machine learning
team of the University of Applied Sciences, Western Switzerland, led by
Prof. Alexandros Kalousis, and will also be associated with the Computer
Science department of the University of Geneva within the VIPER
<http://viper.unige.ch> group led by Prof. Stephane Marchand-Maillet.
Our research explores a number of different issues such as: learning in
high dimensional settings, dimensionality reduction and feature
selection, learning with structured data (multiple kernel learning),
metric and similarity learning, the exploitation of domain knowledge in
the learning process. For a more detailed description the interested
candidates may take a look at: http://cui.unige.ch/~kalousis/
<http://cui.unige.ch/%7Ekalousis/> and the list of publications
<http://cui.unige.ch/%7Ekalousis/htmlStaff/publications> within there.
The greater Geneva lake area is a world-renowned education and research
hub, including not only the University of Geneva, but also EPFL, and
IDIAP. It offers considerable opportunities for training and exposure to
data mining and machine learning, with a number of research teams being
active on these and related fields. In addition the selected candidate
will have ample opportunities to participate in the main ML and DM
conferences.
The ideal candidate will have:
* A PhD on machine learning, data mining or other strongly related
discipline.
* A very solid background in a combination of computer science and
mathematics. Special areas of interest include: statistical machine
learning, statistics, mathematical optimization, mathematical
modelling.
* Strong publication record in the area of machine learning and data
mining (e.g. ICML, NIPS, KDD, IDCM etc).
* Project experience in the area of distributed streaming data will be
a considerable plus.
* Solid expertise in at least one of Matlab or R.
* Solid programming skills in scripting languages, such as perl,
python, etc.
* Excellent command of English.
* Team work capacity.
Candidates should send:
1.A two page CV.
2.A one page motivation letter explaining why their skills, knowledge
and experience make them a particularly suitable candidate for the given
position.
4.A 1000 words research proposal on learning over distributed streaming
data.
4.Their three most representative papers.
5.The *contact details * of three referees; do *not * send reference
letters.
to alexandros.kalou...@hesge.ch
*Application Deadline *
Priority will be given to applications received by the
30/November/2014^th , however applications will be accepted until the
position is filled. The position status will be indicated here
<http://cui.unige.ch/%7Ekalousis/htmlStaff/jobs.htm>
The position will be available from the 1^st of January 2015 with a
possibility for a later start if necessary.
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