We are looking for an excellent candidate who will undertake a PhD on
the development of new machine learning methods for non-parametric
representation learning with an emphasis on two-level instance
description problems and recommendation problems.
In the standard propositional learning scenario instances are described
by a fixed set of attributes, which take values from some domain.
However in many applications the attributes have also their own
descriptions. One of the classical examples is diagnostic problems in
genomics and protemics in which individuals are described by the
expression levels of sets of genes and proteins, which in their turn are
described by their properties. The typical learning methods do not make
use of the available side information. Similar settings appear in
recommender problems in which items and users are also described by side
information. The goal of the project is to develop representation
learning methods that work in a two level setting, the base instance
representation level and the second level that contains the side
information on the attributes, to learn latent representations
exploiting all the available information. To that end the applicant is
expected to build on our expertise on metric and kernel learning, [2]
<http://cui.unige.ch/%7Ekalousis/htmlStaff/job-ML2014.htm#refs3>, [3]
<http://cui.unige.ch/%7Ekalousis/htmlStaff/job-ML2014.htm#refs4>, as
well as to explore the use of deep learning methods, [1]
<http://cui.unige.ch/%7Ekalousis/htmlStaff/job-ML2014.htm#refs1>.
The position is partially funded by a Swiss National Science Foundation
international collaboration project as well as other sources for a
period of three years. The first year brut salary is 47000CHF (the
standard SNSF funding rate for doctorate studens). There will be a
possibility to completement the above amount, for that knowledge of
French would be a plus.
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 enroll as a PhD student at 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 very solid background in a combination of mathematics and computer
science. Special areas of interest include: statistical machine
learning, statistics, mathematical optimization, mathematical
modelling.
* He or she should have completed, or about to complete, an MSc in the
above areas.
* A very good understanding of machine learning methods and
algorithms; project experience in the area will be a considerable plus.
* Solid expertise in at least one of Matlab or R.
* Strong 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.
3.The academic transcripts of their studies.
4.A 500 words research proposal on the project's topic.
5.The *contact details * of three referees; do *not * send reference
letters.
to [email protected] <mailto:[email protected]>,
note that I will be present at ICML 2014 in Beijing and I will be happy
to meet potential candidates there.
*Application Deadline *
Priority will be given to applications send by the 30^th of June 2014,
however applications will be accepted until the position is filled. The
position status will be indicated here:
http://cui.unige.ch/~kalousis/htmlStaff/jobs.htm<http://cui.unige.ch/%7Ekalousis/htmlStaff/jobs.htm>
The position will be available from the 1^st of September 2014 with a
possibility for a later start if necessary.
*References
* [1] Qi Y, Oja M, Weston J, Noble WS. A Unified Multitask Architecture
for Predicting Local Protein Properties, PLoS ONE 7(3) 2012
[2] Phong Nguyen, Jun Wang and Melanie Hilario and Alexandros Kalousis.
Learning heterogeneous similarity measures for hybrid-recommendations in
meta-mining, ICDM 2012
[3] Jun Wang, Huyen Do, Adam Woznica and Alexandros Kalousis. Metric
learning with multiple kernels, NIPS 2011.
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