Researcher position, Statistical Machine Translation

The Machine Learning for Document Access and Translation group of the
Xerox Research Centre Europe conducts research in Statistical Machine
Translation and Information Retrieval, Categorization and Clustering
using advanced machine learning methods. The team is part of the
PASCAL 2 European Network of Excellence, ensuring a strong network of
academic collaboration.

We are opening a position for a researcher with a background in
Statistical Machine Translation to support our participation in the
EU-funded project TransLectures.

Required experience and qualifications:

– PhD in computer science or computational linguistics with focus on
SMT or statistical NLP.
– Good publication record and evidence of implementing systems.
– A good command of English, as well as open-mindedness and the will
to collaborate within a team.
– Acquaintance with Spoken Language Translation is a plus.

Preferred starting date: November 2011

Contract duration: 18 months

Application instructions

Please email your CV and covering letter, with message subject
“Statistical Machine Translation Researcher” to xrce-candidates and to
Nicola.Cancedda at xrce.xerox.com. Inquiries can be sent to
Nicola.Cancedda at xrce.xerox.com.

XRCE is a highly innovative place and we strongly encourage
publication and interaction with the scientific community.

Job announcement URL:

http://www.xrce.xerox.com/About-XRCE/Career-opportunities/Researcher-Statistical-Machine-Translation

CFP: OPT 2011, NIPS Workshop on Optimization for Machine Learning

Dear Colleagues,

We invite participation in the 4th International Workshop on “Optimization for
Machine Learning”, to be held as a part of the NIPS 2011 conference.

Join us for an exciting program that includes plenary talks by:

* Stephen Boyd (Stanford University)
* Aharon Ben-Tal (Technion)
* Ben Recht (UW Madison)

OPT 2011: NIPS Workshop on Optimization for Machine Learning
URL: http://opt.kyb.tuebingen.mpg.de/index.html
Submit: http://www.easychair.org/conferences/?conf=opt2011

Research contributions from the community are welcomed; in particular, we
invite the following two types of submissions:

(i) contributed talks and posters
(ii) open problems

To encourage authors to submit cutting-edge work, the workshop will offer a
best paper award as recognition. We request submitters of open problems to
prepare a few slides that clearly present, motivate, and explain an important
open problem or concern.

The main topics are, including, but not limited to:

* Stochastic, Parallel and Online Optimization,
– Large-scale learning, massive data sets
– Distributed algorithms
– Optimization on massively parallel architectures
– Optimization using GPUs, Streaming algorithms
– Decomposition for large-scale, message-passing and online learning
– Stochastic approximation
– Randomized algorithms

* Nonconvex Optimization,
– Nonsmooth, nonconvex optimization
– Nonconvex quadratic programming, including binary QPs
– Convex Concave Decompositions, D.C. Programming, EM
– Training of deep architectures and large hidden variable models
– Approximation Algorithms

* Algorithms and Techniques (application oriented)
– Global and Lipschitz optimization
– Algorithms for nonsmooth optimization
– Linear and higher-order relaxations
– Polyhedral combinatorics applications to ML problems

* Combinatorial Optimization
– Optimization in Graphical Models
– Structure learning
– MAP estimation in continuous and discrete random fields
– Clustering and graph-partitioning
– Semi-supervised and multiple-instance learning

* Practical techniques
– Optimization software and toolboxes
– GPU, Multicore, Distributed implementations

* Applications close to machine learning
– Sparse learning, compressed sensing, signal processing
– Computational Statistics
– Large scale scientific computing

Important Dates
—————

* Deadline for submission of papers: 26th October 2011
* Notification of acceptance: 12th November 2011
* Final version of submission: 24th November 2011

Please note that at least one author of each accepted paper must be available
to present the paper at the workshop. Further details regarding the
submission process are available at the workshop homepage.

Organizers
———-

* Suvrit Sra, Max Planck Institute for Intelligent Systems
* Sebastian Nowozin, Microsoft Research, Cambridge, UK
* Stephen Wright, University of Wisconsin, Madison

Further Details
—————

http://opt.kyb.tuebingen.mpg.de/index.html

Post-doc position in machine learning for Human-Robot social interaction

The IDIAP Research Institute (www.idiap.ch), a laboratory associated
with EPFL (Swiss Federal Institute of Technology, Lausanne) seeks
qualified candidates for

1 postdoctoral research position in computer vision and machine learning
for multi-modal interaction modeling.

The research will be conducted in the context of the HUMAVIPS
(http://humavips.inrialpes.fr/) project, a 3-year project funded by the
European Community. The overall goal of the project is to endow a
humanoid robot (NAO) with audio-visual sensing and interaction
capabilities allowing him to navigate in a complex environment,
localize a group of people, join it and interact with it.

The research done at Idiap focus on the design of multimodal perceptual
algorithms to recognize human non-verbal behaviors (when people speak,
whom they look at), investigate new approaches to model and identify
people interactions and relationships (e.g. who speaks to whom, who is
the most important person of the group) and exploit the recognized
conversational patterns and relationship in a robot-to-group of people
multimodal interface design.

The postdoctoral researcher will address one of the above theme.
She/he should have a strong background in statistics, applied
mathematics,
and computer vision. Experience in one or several of the following
areas is required:
– computer vision and tracking
– event recognition and discovery
– human-robot or human-computer multimodal interface design
The applicant should be familiar with C/C++ programming and the Linux
environment.
The position offers the opportunity to collaborate with the best
research teams in Europe,
and involvement in PhD supervision.

Contract:
Starting date: as soon as possible.
The initial appointment will be until the end of the project (31/01/2013).

The salary ranges from CHF 70 000 to 75 000/year according to training
and previous experience.

Application:
————–
For further details and application please contact:

Jean-Marc Odobez (odobez(at)idiap.ch, tel : +41 (0)27 721 77 26)

About IDIAP:
————
Idiap is an independent, non-profit research institute recognized and
supported by the Swiss Government, and affiliated with the Ecole
Polytechnique
Fédérale de Lausanne (EPFL).
It is located in the town of Martigny in Valais, a scenic region in the
south of Switzerland, surrounded by the highest mountains of Europe,
and offering exciting recreational activities, including hiking,
climbing and skiing, as well as varied cultural activities.
It is within close proximity to Geneva and Lausanne.
Although Idiap is located in the French part of Switzerland, English is
the working language. Free French lessons are provided.

Idiap offers competitive salaries and conditions at all levels in a young,
dynamic, and multicultural environment. Idiap is an equal opportunity
employer
and is actively involved in the “Advancement of Women in Science” European
initiative. The Institute seeks to maintain a principle of open competition
(on the basis of merit) to appoint the best candidate, provides equal
opportunity for all candidates, and equally encourage both genders to
apply.

Postdoctoral position in computational neuroscience available

University of Surrey

Faculty of Engineering and Physical Sciences

Department of Computing

Research Fellow

Salary: Up to £32751 per annum
(subject to qualifications and experience)

We are seeking a Research Fellow to work on a cross-disciplinary research project in the fields of computational neuroscience, computational systems biology and computational intelligence.

You will work in the Nature Inspired Computing and Engineering (NICE) Group within the Computing Department.

You will have a PhD (or equivalent) in computer science, computational neuroscience, or electrical engineering and a strong interest in computational neuroscience, computational biology and computational intelligence. Expertise in spiking neural network based reservoir computing is essential; knowledge in activity-dependent plasticity or computational modelling of gene regulatory networks is a plus. Programming skills in C/C++ and Matlab is also highly desirable.

Start date: 1st December 2011 or as soon as possible thereafter. The appointment is for 13 months with an extension possibility depending on the availability of further funding.

Informal enquires to Prof. Yaochu Jin (e-mail: yaochu.jin(at)surrey.ac.uk, tel: +44 (0) 1483 686037).

For an application pack and to apply on-line please go to our website: www.surrey.ac.uk/vacancies
If you are unable to apply on-line please contact Mr Peter Li, HR Assistant on Tel:
+44 (0) 1483 686060 or email: K.Li(at)surrey.ac.uk.

Closing date for applications is Friday, 4th November 2011

For further information about the University of Surrey, please visit www.surrey.ac.uk

We acknowledge, understand and embrace cultural diversity

CFP: Deep Hierarchies in Vision

Workshop on Deep Hierarchies in Vision
(in conjunction with CogSys2012 – 5th Int. Conf. on Cognitive Systems)
Webpage: http://www.kovan.ceng.metu.edu.tr/~sinan/DHV/

=> February 21, 2012; Vienna-Austria <= Organizers: Ales Leonardis, Norbert Krueger, Richard Bowden, Sinan Kalkan, Nicolas Pugeault, Frank Guerin Webpage for the 5th Int. Conf. on Cognitive Systems: http://cogsys2012.acin.tuwien.ac.at/ Overview: Processing in the brain in general and visual processing in particular is organized in a hierarchical fashion, from simple localized features to complex, large scale features. The visual system consists of a hierarchy, in which neurons in early visual areas extract simple image features (orientation, motion, disparity) over a small local region of visual space, which are then transmitted to neurons in higher visual areas responding to more complex features (e.g. shape) over a larger region of visual space. Hierarchical representations can derive and organize features at multiple levels. Hierarchical representations with many levels are called 'deep hierarchies'. They build on top of each other by exploiting the shareability of features among more complex compositions or objects themselves. Sharing features, on the one hand, means sharing common computations, which brings about (highly desirable) computational efficiency. On the other hand, reusing the commonalities between objects’ models places their representations in relation to other objects, thus leading to high generalization capabilities and lower storage demands. However, although all neurophysiologic evidence suggests that in the human visual system quite a number of levels are realized, it has turned out that the design and/or learning of such deep hierarchical systems is a very difficult task. Most existing computer vision systems are 'flat' (e.g., having rather simple features - such as SIFT - as input and then applying some kind of SVM learning) and hence cannot make use of the advantages connected to deep hierarchies. Here in particular the generalization capabilities are crucial for any form of cognitive intelligence. As a consequence, we see the issue of establishing deep hierarchies as one major challenge for the establishment of truly cognitive systems. The aim of the workshop is to bring together researchers from vision, robotics, machine learning, artificial intelligence, and neurophysiology to discuss existing obstacles in the design of deep hierarchies, possible solutions as well as perspectives for deep hierarchies in vision and robotics. A special issue with contributions from the workshop on 'Deep Hierarchies in Vision' in a journal is planned. ===================== Invited speakers: ===================== Christian Igel The Image Group, University of Copenhagen, Denmark Justus Piater Department of Electrical Engineering and Computer Science University of Innsbruck, Austria Laurenz Wiskott Institut für Neuroinformatik Ruhr-Universität Bochum, Germany Peter Janssen Division of Neurophysiology Katholieke Universiteit Leuven, Belgium ===================== Submission: ===================== We invite two-page extended abstracts until the 1st of December, 2011. We provide a Latex template for two-page extended abstracts at: http://www.kovan.ceng.metu.edu.tr/~sinan/DHV/. Please follow this template and submit a PDF file online at our workshop website. The submissions will be reviewed by three reviewers, and the decisions will be announced by the 20th of December, 2011. A special issue with contributions from the workshop on 'Deep Hierarchies in Vision' in a journal is planned. ===================== Important Dates: ===================== Abstract Submission: 01.12.2011 (23:59 UTC/GMT) Notification of Acceptance: 20.12.2011 Camera-ready Submission: 15.01.2012 Workshop: 21.02.2012 ===================== Program Co-chairs: ===================== Laurenz Wiskott Institut für Neuroinformatik Ruhr-Universität Bochum, Germany Peter Janssen Division of Neurophysiology Katholieke Universiteit Leuven, Belgium ===================== Contacts: ===================== Ales Leonardis, ales.leonardis(at)fri.uni-lj.si Norbert Krueger, norbert(at)mmmi.sdu.dk Sinan Kalkan, skalkan(at)ceng.metu.edu.tr

Copulas in Machine Learning– NIPS-2011 Workshop — Call for Abstracts

NIPS-2011 Workshop, Granada, Spain December 16th, 2011
http://pluto.huji.ac.il/~galelidan/CopulaWorkshop
DESCRIPTION

From high-throughput biology and astronomy to voice analysis and medical diagnosis,
a wide variety of complex domains are inherently continuous and high dimensional.
The statistical framework of copulas offers a flexible tool for modeling highly non-linear
multivariate distributions for continuous data. Copulas are a theoretically and
practically important tool from statistics that explicitly allows one to separate the
dependency structure between random variables from their marginal distributions.
Although bivariate copulas are a widely used tool in finance, and have even been famously
accused of “bringing the world financial system to its knees” (Wired Magazine, 2009),
the use of copulas for high dimensional data is in its infancy.

While studied in statistics for many years, copulas have only recently been noticed by a
number of machine learning researchers, with this “new” tool appearing in the recent
leading machine learning conferences (ICML, UAI and NIPS). The goal of this workshop is
to promote the further understanding and development of copulas for the kinds of complex
modeling tasks that are the focus of machine learning. Specifically, the goals of the workshop are to:

· draw the attention of machine learning researchers to the important framework of copulas

· provide a theoretical and practical introduction to copulas

· identify promising research problems in machine learning that could exploit copulas

· bring together researchers from statistics and machine learning working in this area

The target audience includes leading researchers from academia and industry, with the aim of facilitating cross fertilization between different perspectives.
CALL FOR ABSTRACTS

We invite submission of abstracts to the workshop. Abstracts will be selected for a short oral or poster presentation.

· Abstracts should be submitted no later than Friday, October 21st, 2011.

· Abstracts should be at most 2 pages long in the NIPS format.

· Abstracts should be submitted by email to galel at huji dot ac dot il
DATES

Submission deadline: October 21st, 2011. Notification of acceptance: November 4th, 2011.
ORGANIZERS

Gal Elidan, The Hebrew University of Jeruslaem
Zoubin Ghahramani Cambridge University and Carnegie Mellon University
John Lafferty, University of Chicago and Carnegie Mellon University

PhD Studentships in Probabilistic Machine Learning

A number of PhD studentships are available in the

Probabilistic Machine Learning group [headed by Matthias Seeger]
School of Computer and Communication Sciences
Ecole Polytechnique Federale de Lausanne (EPFL)
http://lapmal.epfl.ch/

The group focusses on the development and analysis of scalable Bayesian
inference and graphical modelling technology, with applications to Bayesian
experimental design (adaptive compressive sensing, active learning), Bayesian
learning and robust estimation.
Advances are applied to challenging problems in magnetic resonance imaging
(sampling optimization, motion compensation, phase-sensitive imaging,
parallel MRI, parallel transmit), in collaboration with leading MRI research
groups (CIBM, Lausanne; MPI, Tuebingen), as well as to low-level computer
vision and other high-dimensional scenarios.

The group is part of one of Europe’s highest ranked computer science faculties
at EPFL, one of the leading technical universities worldwide, a unique
surrounding for study and research. EPFL is beautifully located between
Lake Geneva and a stunning mountain scenery, offering great opportunities for
the outdoors. The Lausanne area is known for its numerous cultural festivals.

Openings are available for exceptional students with excellent mathematical
background and high motivation for research in probabilistic machine learning,
approximate Bayesian inference, and applications thereof. Admission to the
doctoral program is internationally competitive.

Application to the doctoral program EDIC is centralized. Please refer to

http://phd.epfl.ch/page-19698-en.html

for any details concerning the application process.

*** Do NOT reply to this mail or send me your documents, but submit them
*** through the site. Applications which are not centrally submitted,
*** cannot be considered.
*** Please indicate in your submission that you would want to work with
*** me (listing other faculty as well is perfectly fine), as this will
*** flag your application for me. You may want to indicate to me that you
*** have applied, but please do not expect a direct answer.

The deadline for applications is

January 15, 2012

Relevant links:

– EDIC doctoral school:
http://phd.epfl.ch/page-19698-en.html
– Research in the Probabilistic Machine Learning group:
http://lapmal.epfl.ch/
– Computer and Communication Sciences, EPFL:
http://ic.epfl.ch/

PhD Position in Medical Data Mining: Predicting Response to Therapy in Heart Failure by Using a Multiple Biomarker Panel

Applications are invited for a full-time 4-year PhD research
position shared by the Department of Knowledge Engineering,
Maastricht University, Maastricht, the Netherlands, and the
Heart Failure Clinic of the Maastricht University Medical
Centre, Maastricht, the Netherlands, starting from October-
November, 2011.

The PhD project aims at discovering multiple biomarker panels
from heart-patient data using data-mining techniques. The
final goal is to predict individual outcome and to define
therapy beneficial for individual heart patients.

The PhD student is expected to work on the following main
tasks:
(1) Reviewing, analyzing, and adapting state-of-the-art
data-mining approaches for feature selection, feature
construction, bio-marker discovery, and classification;
(2) Discovering multiple biomarker panels for heart
patients;
(3) Predicting outcome and defining therapy for individual
patients.

Requirements

The PhD student is expected to meet the following requirements:

* A master’s degree (or equivalent) in Computer Science,
Statistics, Data Mining, or a related field, with a
strong interest in feature selection/construction, and
bio-marker discovery;
* Programming skills: Java/C, Matlab;
* An enthusiastic and cooperative working attitude;
* Good didactic abilities.
* Excellent proficiency in written and spoken English.

Conditions of employment

MaastrichtUniversityare set out in the Collective Labour Agreement
of Dutch Universities (CAO). Furthermore, local UM provisions also
apply. For more information look at the website
www.maastrichtuniversity.nl/, A-Z Terms of Employment.

Monthly gross salary during the first year will be: 2,042 euros and
during the fourth year 2,612 euros

Employment basis: 4 years Maximum hours per week: 38

Contract Type: Temporary for 48 months

Organization

Maastricht University (UM) is renowned for its unique, innovative,
problem-based learning system, which is characterized by a
small-scale and student-oriented approach. Research at UM is
characterized by a multidisciplinary and thematic approach, and
is concentrated in research institutes and schools. Maastricht
University has around 14,000 students and 3,500 employees. Reflecting
the university’s strong international profile, a fair amount of both
students and staff are from abroad. The university is comprised of
6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty
of Law, School of Business and Economics, Faculty of Humanities and
Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology
and Neuroscience.

http://www.maastrichtuniversity.nl/

Department

Departement of Knowledge Engineering (DKE)

www.maastrichtuniversity.nl/web/Schools/DKE.htm

Research group BioMathematics and BioInformatics (BMI) within DKE:

www.maastrichtuniversity.nl/web/Schools/DKE/Thema/Research/BMI.htm

Additional information about the vacancy can be obtained from:

Dr. Evgueni Smirnov
Tel.: +31 43 38 82023
Email: smirnov(at)maastrichtuniversity.nl

Call for Participation – Bridging statistical physics and optimization, inference and learning Workshop

Dear Colleague,

It is our pleasure to announce that in February 19-24, 2012 the Les Houches center for theoretical physics will be hosting a workshop “Bridging statistical physics and optimization, inference and learning”
organized by Silvio Franz, Florent Krzakala, Giorgio Parisi, Federico Ricci-Tersenghi and Lenka Zdeborova.

This workshop is an informal continuation of similar programs on “Statistical physics of disordered systems and its applications” hosted in Les Houches in winter in the past years (February 2005, February 2006, and March 2010). This time the workshop will focus on application of statistical physics techniques to important problems in optimization, inference and learning. More information and a list of confirmed invited speakers can be found on the workshop website:

http://www.espci.fr/usr/fk/LESHOUCHES/home.htm

We would like to encourage all researchers in the domain, and in particular PhD students and postdocs to participate at the workshop and to apply for a contributed talk. All wishing to participate (to give a talk) have to register (to apply) at the workshop website by November 14, 2011.

With best regards

The organizers

Silvio Franz,
Florent Krzakala,
Giorgio Parisi,
Federico Ricci-Tersenghi,
Lenka Zdeborova

CALL FOR DEMOS – EACL 2012 – Avignon, France, April 23-27, 2012

== EACL 2012 – CALL FOR DEMOS ==
The Thirteenth Conference of the European Chapter of
the Association for Computational Linguistics
http://eacl2012.org/system-demonstration/index.html

Avignon, France
April 23-27, 2012

EACL 2012 features a special session for demonstrations. Topics
of interest cover all aspects of computational linguistics, as
outlined in the main conference call for papers. Demos will be
presented at specific sessions from the main program of the
conference. The demo track allows researchers and practitioners
to demonstrate their new and innovative systems while interacting
with their audience in an informal setting. Demos’ submissions
should be concerned with mature systems or prototypes in which
computational linguistics or NLP technologies are used to solve
practically important problems. Demo papers should make clear
which aspects of the system will be demonstrated, and how the
system is used to solve practical problems. It should include
a discussion of the implementation and case studies of how
the system is applied.

== Requirements ==
At least one author of each accepted demo paper must register
for and attend the conference, and demonstrate the system during
the demo sessions (please see the Conference Policies). Authors
of accepted demos may also be required to demonstrate their systems
at additional events during the conference. Each accepted demo
paper will be allocated four pages in the conference proceedings;
no extra pages can be purchased. Demo authors are not required to
transfer copyright.

== Submission Procedure ==
Authors of demos should submit an original paper describing their
proposed presentation. The length of the paper is limited to 4 pages
excluding references which are unlimited. The submission should be in
line with the EACL 2012 main conference formatting styles and instructions
for authors.

Proposals for demos should provide:
1. an overview of what the demonstration aims to achieve
2. how the demo illustrates novel ideas
3. any URLs that link to screen-shots, live demos, or related information
4. equipment or facilities required for the system demonstration

== Reviewing ==
Demos will be blind peer-reviewed by two members of the Demos Program
Committee, who will judge the originality, significance, quality, and
clarity of each submission.

== Demos Chair ==
Frédérique Segond (Xerox Research Centre Europe, France)
Contact: segond(at)xrce.xerox.com

== PC Members ==
Sophia Annaniadou (University of Manchester, UK)
Galia Angelova, (Bulgarian Academy of Sciences, Bulgaria )
Raffaella Bernardi (University of Trento, Italy
Luca Dini (CELI , Italy)
Piek Vossen (Vrije Universiteit of Amsterdam, Nederland)
Stelios Piperidis (ILSP, Greece)
Sebastian Padó (University of Heidelberg, Germany)
Evelyne Viegas (Microsoft, USA)
Claire Waast (EDF France)

== Important Dates ==
* Paper submission deadline: December, 4, 2011 (11:59pm Samoa Time (UTC/GMT -11 hours))
* Notification of acceptance: February, 6, 2012
* Camera ready copies due: March 9, 2012
* EACL 2012 Conference: April 23-27, 2012