PASCAL2 Posts

XRCE Internship: Optimization and Sampling Techniques for Statistical Machine Translation

See: http://www-int.xrce.xerox.com/About-XRCE/Internships/Optimization-and-Sampling-Techniques-for-Statistical-Machine-Translation

Start Date : Around June 2012

Duration : 4-5 months

The MLDAT area (Machine Learning for Document Access and Translation) at XRCE (Xerox Research Centre Europe) is opening an internship to pursue its current research line in Optimization and Sampling Techniques for Statistical Machine Translation.

XRCE has developed some algorithms, which combine optimization and sampling in novel ways in order to perform inference and training for a number of NLP tasks, in the presence of complex feature spaces.

We are looking for a motivated intern to pursue this line of work, further implement these techniques and perform experiments in the context of phrase-based and/or syntax-based translation.

The successful candidate should be enrolled in a graduate program, at the Master or (preferably) PhD level, with focus on Machine Learning, Optimization, Statistical NLP, or (ideally) Statistical Machine Translation.

Strong programming skills (one or several of C/C++, Python, Java…) are a requirement.

IMPORTANT. Priority will be given to students who are members of institutions affiliated to the PASCAL-2 network of excellence. A partial list of such institutions is available at: http://pascallin2.ecs.soton.ac.uk/Network/Sites .

For further details, please contact Marc Dymetman (marc.dymetman@xrce.xerox.com).

Fully funded PhD position in reinforcement learning for information retrieval at the University of Amsterdam

The Informatics Institute at the University of Amsterdam invites applications for a fully funded position for a PhD student in the area of reinforcement learning for information retrieval. The position is within the Intelligent Systems Lab Amsterdam and will be supervised by dr. Shimon Whiteson and prof. dr. Maarten de Rijke.

Application closing date: 30 April 2012
Starting date: 15 July 2012 (later starting date is possible).
Duration: 4 years.

The research will focus on the development of new algorithms for modeling and learning from implicit feedback, such as click behavior, in order to optimize the behavior of information retrieval systems. Doing so will require new reinforcement learning techniques, as well as other types of machine learning. The new methods will be applied to real data from a leading Dutch social networking site. The project is funded by a grant from the Netherlands Organization for Scientific Research.

Applicants must have a master’s degree in computer science or a closely related area.

In addition, a successful candidate should have:

* strong math skills.

* good knowledge of modern machine learning methods (specific knowledge of reinforcement learning and/or decision-theoretic planning is a plus).

* experience programming in at least one of the following languages: C, C++, Java, Python, or Perl.

* excellent oral and written communication skills.

The successful candidate will be based in the Intelligent Systems Lab Amsterdam (ISLA) within the Informatics Institute at the University of Amsterdam. The institute was recently ranked among the top 50 computer science departments in the world by the 2011 QS World University IT Rankings. ISLA consists of 20 members of faculty, 20 post-doctoral researchers, and more than 50 PhD students. Members of the lab are actively pursuing a variety of research initiatives, including machine learning, decision-theoretic planning and learning, multiagent systems, human-computer-interaction, natural language processing, information retrieval, and computer vision.

Some of the things we have to offer:

* competitive pay and excellent benefits
* extremely friendly working environment
* high-level of interaction
* location near the city center (10 minutes by bicycle) of one Europe’s most beautiful and lively cities
* international environment (10+ nationalities in the group)
* access to high-end computing facilities (cluster with 4,000+ cores)
* brand-new building

Since Amsterdam is a very international city where almost everybody speaks and understands English, candidates need not be afraid of the language barrier.

For further information, including instructions on submitting an application, see the official job ad at http://tinyurl.com/6od9ulb

Informal inquiries can be made by email to Shimon Whiteson (s.a.whiteson@uva.nl).

Research Associate in Statistics – Ref:1239870

University College London
Department of Statistical Science
www.ucl.ac.uk/stats

Salary (inclusive of London allowance)
£32,055-£36,690 per annum

This is a superb opportunity for an ambitious postdoctoral research associate who will be required to innovate and develop the theory, methods and algorithms defining a range of statistical models, along with associated inferential machinery necessary to represent then analyse the dynamics of the ‘software population’.

Duties and Responsibilities
Applications are invited for a postdoctoral research associate to work with Professor Mark Girolami on the EPSRC Research Programme grant “A Population Approach to Ubicomp System Design”. The primary aim of this project is to deliver a new science of software structures, with theory and tools that reflect software in real world use, and are able to tackle the complex problem of how to design, support change and appropriation. The key concept is the ‘statistical software population’: a statistical model of the variation observed when we look at how the same initial program has been used and adapted by its users. A statistical population model is kept up to date by logging each instance of a program, analysing how it is used and changed both temporally and spatially.

The post is available from April 2012 (or as soon as possible thereafter) and is funded until 1 December 2014 in the first instance with possible extension of a further two years.

Key Requirements
Candidates should have a PhD (or equivalent qualification) and research background in Statistical Methodology or Machine Learning. It is essential that the successful candidate has extensive experience of Computational Statistics, Statistical Modelling, and Bayesian methods in Pattern Analysis.

Further Details
Informal enquiries may be addressed to Professor Mark Girolami, email: m.girolami@ucl.ac.uk, tel: +44(0)20 7679 1861.

Department of Statistical Science
The Computational Statistics research group at UCL offers a vibrant and intellectually stimulating environment for individuals who wish to develop their careers in a world class research environment. UCL is ranked among the top ten research institutions worldwide and has unique strengths in Computational Statistics and Machine Learning. Together with the Gatsby Institute for Computational Neuroscience at UCL, the Departments of Statistical Science and Computer Science form the Centre for Computational Statistics and Machine Learning, (http://www.csml.ucl.ac.uk/) which is part of the European network PASCAL (http://www.pascal-network.org/).

For any queries regarding the vacancy or the application process please contact Dr Russell Evans, email: russell.evans@ucl.ac.uk, tel: +44 (0)20 7679 8311.

https://atsv7.wcn.co.uk/search_engine/jobs.cgi?owner=5041178&ownertype=fair&jcode=1239870
https://www.ucl.ac.uk/statistics/department/jobs/

NIPS 2012 Call for Papers

Neural Information Processing Systems Conference and Workshops
December 3-8, 2012
Lake Tahoe, Nevada, USA
http://nips.cc/Conferences/2012/

Deadline for Paper Submissions: Friday, June 1, 2012, 11 pm Universal
Time (4 pm Pacific Daylight Time). Submit at:
https://cmt.research.microsoft.com/NIPS2012/

Submissions are solicited for the Twenty-Sixth Annual Conference on
Neural Information Processing Systems, an interdisciplinary conference
that brings together researchers in all aspects of neural and
statistical information processing and computation, and their
applications. The conference is a highly selective, single track
meeting that includes invited talks as well as oral and poster
presentations of refereed papers. Submissions by authors who are new to
NIPS are encouraged. The 2012 conference will be held on December 3-6
at Lake Tahoe, Nevada. One day of tutorials (December 3) will precede
the main conference, and two days of workshops (December 7-8) will
follow it at the same location.

Technical Areas: Papers are solicited in all areas of neural
information processing and statistical learning, including, but not
limited to:

* Algorithms and Architectures: statistical learning algorithms, kernel
methods, graphical models, Gaussian processes, neural networks,
dimensionality reduction and manifold learning, model selection,
combinatorial optimization, relational and structured learning.

* Applications: innovative applications that use machine learning,
including systems for time series prediction, bioinformatics, systems
biology, text/web analysis, multimedia processing, and robotics.

* Brain Imaging: neuroimaging, cognitive neuroscience, EEG
(electroencephalogram), ERP (event related potentials), MEG
(magnetoencephalogram), fMRI (functional magnetic resonance imaging),
brain mapping, brain segmentation, brain computer interfaces.

* Cognitive Science and Artificial Intelligence: theoretical,
computational, or experimental studies of perception, psychophysics,
human or animal learning, memory, reasoning, problem solving, natural
language processing, and neuropsychology.

* Control and Reinforcement Learning: decision and control,
exploration, planning, navigation, Markov decision processes, game
playing, multi-agent coordination, computational models of classical
and operant conditioning.

* Hardware Technologies: analog and digital VLSI, neuromorphic
engineering, computational sensors and actuators, microrobotics,
bioMEMS, neural prostheses, photonics, molecular and quantum computing.

* Learning Theory: generalization, regularization and model selection,
Bayesian learning, spaces of functions and kernels, statistical physics
of learning, online learning and competitive analysis, hardness of
learning and approximations, statistical theory, large deviations and
asymptotic analysis, information theory.

* Neuroscience: theoretical and experimental studies of processing and
transmission of information in biological neurons and networks,
including spike train generation, synaptic modulation, plasticity and
adaptation.

* Speech and Signal Processing: recognition, coding, synthesis,
denoising, segmentation, source separation, auditory perception,
psychoacoustics, dynamical systems, recurrent networks, language
models, dynamic and temporal models.

* Visual Processing: biological and machine vision, image processing
and coding, segmentation, object detection and recognition, motion
detection and tracking, visual psychophysics, visual scene analysis and
interpretation.

Evaluation Criteria: Submissions will be refereed on the basis of
technical quality, novelty, potential impact, and clarity.

Submission Instructions: All submissions will be made electronically,
in PDF format. As in previous years, reviewing will be double-blind:
the reviewers will not know the identities of the authors. Papers are
limited to eight pages, including figures and tables, in the NIPS
style. An additional ninth page containing only cited references is
allowed. Complete submission and formatting instructions, including
style files, are available from the NIPS website, http://nips.cc.

Supplementary Material: Authors can submit up to 10 MB of material,
containing proofs, audio, images, video, data or source code. Note that
the reviewers and the program committee reserve the right to judge the
paper solely on the basis of the 9 pages of the paper; looking at any
extra material is up to the discretion of the reviewers and is not
required.

Submission process: Electronic submissions will be accepted until
Friday, June 1, 2012, 11 pm Universal Time (4 pm Pacific Daylight
Time). As was the case last year, final papers will be due in advance
of the conference.

Dual Submissions Policy: Submissions that are identical (or
substantially similar) to versions that have been previously published,
or accepted for publication, or during the NIPS review period are in
submission to another peer-reviewed and published venue are not
appropriate for NIPS, with three exceptions listed below. These
exceptions, which have been approved by the NIPS Foundation board in
the interests of speeding up scientific communication and improving the
efficiency of peer review, are as follows:
1.Concurrent submission to other venues is acceptable provided that:
(a) The concurrent submission or intention to submit to other venues is
declared to all venues, (b) NIPS and the concurrent venues are given
permission by the author(s) to coordinate reviewing, and (c) acceptance
to one venue imposes withdrawal from all other venues with the
exception stated in 2 below.
2.NIPS submissions that summarize a longer journal paper, whether
published, accepted, or in submission, are acceptable if the authors
inform NIPS and the journal and give them permission to coordinate
reviewing.
3.It is acceptable to submit to NIPS 2012 work that has been made
available as a technical report (or similar, e.g. in arXiv) as long as
the conditions above are satisfied.

None of the above should be construed as overriding the requirements of
other publishing venues. In addition, keep in mind that author
anonymity to NIPS reviewers might be compromised for authors availing
themselves of exceptions 2 and 3. Authors must declare submissions to
other venues either through the CMT submission form, or via email to
the program chairs at program-chairs@nips.cc.

Authors’ Responsibilities: If there are papers that may appear to
violate any of these conditions, it is the authors’ responsibility to
(1) cite these papers (preserving anonymity), (2) argue in the body of
your paper why your NIPS paper is non-trivially different from these
concurrent submissions, and (3) include anonymized versions of those
papers in the supplemental material.

Demonstrations and Workshops: There is a separate Demonstration track
at NIPS. Authors wishing to submit to the Demonstration track should
consult the Call for Demonstrations. The workshops will be held at Lake
Tahoe, Nevada, December 7-8. The upcoming call for workshop proposals
will provide details.

Web URL: http://nips.cc/Conferences/2012/CallForPapers

Google sponsored post doc Oxford Brookes

Looking for researcher of exceptional talent to conduct 2 year research fellowship to work closely with Professor Philip Torr (http://cms.brookes.ac.uk/staff/PhilipTorr/), at Oxford Brookes vision research group http://cms.brookes.ac.uk/research/visiongroup, and Professor Steve Seitz at Google Research to work on various projects connected with our ongoing work on scene understanding (see here for recent publications http://cms.brookes.ac.uk/staff/PhilipTorr/papers.htm), especially understanding Streetview imagery.

There will be a large amount of freedom for the researcher and it is expected that applicants will have a top flight record, as evinced by publications in top venues such as ICCV, NIPS, ECCV, SIGGRAPH, CVPR, PAMI, JMLR, IJCV etc. The candidate will benefit from close mentoring to encourage them to develop into a top flight researcher.

The computer vision group at Oxford Brookes has a strong international reputation, having taken scientific best paper awards at all the conferences mentioned previously, it is an exceptionally stimulating environment, with strong connections to the top industrial research labs such as Sony, Microsoft and Google, as well as very close links with the Visual Geometry Group at Oxford University with whom we share reading groups and seminars. It is soon to move to brand new facilities near the heart of Oxford, a very academically vibrant city. There will be opportunity as part of the project to travel to the US and work closely with Google research.

The candidate would be expected to have a top flight publication record, demonstrated by publication in top rank venues such as those listed above.

For further information contact philiptorr@brookes.ac.uk (http://cms.brookes.ac.uk/staff/PhilipTorr/)

ImageCLEF provides an evaluation forum for the cross-language annotation and retrieval of images.

Motivated by the need to support multilingual users from a global
community accessing the ever growing body of visual information, the main
goal of ImageCLEF is to support the advancement of the field of visual
media analysis, indexing, classification, and retrieval. To this end,
ImageCLEF develops the necessary infrastructure for the evaluation of
visual information retrieval systems operating in both monolingual,
cross-language and language-independent contexts and provides
reusable resources for such benchmarking purposes.

ImageCLEF 2012 is part of CLEF 2012 (http://clef2012.org/) and organises
four main tasks:
– Medical Image Classification and Retrieval
– Photo Annotation and Retrieval
– Plant identification
– Robot Vision
There will also be an additional pilot task on Personal Photo Retrieval.

Registration is now open! Information on the registration process and this
year’s tasks can be found at: http://imageclef.org/2012

Postdoc position: information diffusion in graphs. INRIA, Lille.

The Mostrare team at INRIA-Lille (http://mostrare.lille.inria.fr/)
invites applications for a full-time post doctoral position in the
area of machine learning for dynamic structured data, starting
(ideally) in June 2012.

Mission :

The research project will be focused on mining data represented as
graphs. Graph data arises in a wide variety of disciplines; for
example, social networks, heterogeneous databases and biosciences.
In this kind of graphs, aspects like multi-modality, dynamicity, or scalability raise new
important challenges that machine learning algorithm has to face.
Information diffusion on graphs consists on the probation of rumors,
news, labels from seed nodes to the rest of the graph.
Many works in the literature have considered
the problem of information diffusion in various domains like social
science, epidemiology, web, physics, marketing and new
applications ranging from advertising or recommendation, community
detection. Many of these works consider the problem of fitting a
diffusion model and try to explain the diffusion process.

In a first step of this work we will restrict ourselves to predict a
final step of this process from a given initial one or vice-versa
finding an initial state observing the result of the final diffusion
process. We will see that important tasks such as classification or
node labeling, link prediction in the setting of semi-supervised
learning can be casted as the information diffusion process. The
generalization will come by defining proper embeddings of the graph
where machine learning algorithms will learn the diffusion process for
the task under consideration.

In a second step, the candidate will also study efficient learning
algorithms and models that not only deal with large sizes of graphs,
but can adapt to the shifting trends of their dynamicity. Methods to
investigate include incremental learning, active learning or sampling
methods among others.

Finally experiments will be conducted on large graphs representing
brain activities and also on other heterogeous databases.

Skills and profile :

Applicants must have already or be very close to obtaining a PhD in
computer science, graph algorithms, machine learning. Some experience
in implementation and experimentation is expected. Fluency in English
is an important added-value. Informal enquiries may be directed by
email to: Gemma Garriga at gemma.garriga@inria.fr or Marc Tommasi at
marc.tommasi@inria.fr

LREC2012 Tutorial: “Bootstrapping ontology evolution: a generic approach relying on ontology-based information extraction”

Monday, 21 May 2012 – Morning Session

8th international conference on Language Resources and Evaluation (LREC) 2012, Istanbul, Turkey (http://www.lrec-conf.org/lrec2012/)

Summary:

————-

This tutorial provides a detailed introduction to the research area of ontology evolution. After a short introduction to the problem of ontology evolution and the presentation of the current state of the art, the tutorial will present in detail the ontology learning approach that has been developed in the context of the BOEMIE EU-funded research project. The tutorial will present an ontology-based information extraction system and how this system is exploited to learn an ontology in a synergetic, semi-automated approach, employing bootstrapping. The third part of the tutorial will focus on how internal information (encoded in instances) and external knowledge sources (i.e. other ontologies and hierarchies) can be exploited in order to enhance proposals for new concepts, through instance matching. Finally, the tutorial will conclude with the state of the art in ontology evaluation, and evaluation results of the described approach on the thematic domain of athletics.

http://www.lrec-conf.org/lrec2012/IMG/tut/programme/LREC2012_BOEMIE_tutorial_outline-v2.pdf

The Presenters:

——————-

Dr. Georgios Petasis (NCSR “Demokritos”, Greece)

Dr. Anastasia Krithara (NCSR “Demokritos”, Greece)

Dr. Alfio Ferrara (University of Milano, Italy)

Dr. Vangelis Karkaletsis (NCSR “Demokritos”, Greece)

Exploration & Exploitation 3 – Challenge

We are happy to announce a new challenge on exploration and exploitation in conjunction with an ICML’2012 workshop.

The dataset provided by Yahoo! contains user click logs for news articles displayed in the Featured Tab of the Today Module on Yahoo!
The goal is to optimize the choice of the article to display with respect to a user description in order to gather the maximum number of clicks.

Key dates :
March 9th : Opening of the Challenge
June 1st: End of the first part of the Challenge
July 1st: Announcement of final result

More information available at: http://explochallenge.inria.fr/

We hope to see you in Edinburgh!

POSTDOC/PHD POSITION IN COMPUTATIONAL LINGUISTICS, HEIDELBERG UNIVERSITY, GERMANY

The chair of Linguistic Informatics at the Institute for Computational Linguistics, Heidelberg University, Germany, invites applications for a research associate position (Phd or PostDoc) in the research project “Cross-language Learning-to-Rank for Patent Retrieval” funded by the Deutsche Forschungsgemeinschaft (DFG).

The goal of the project is to develop a synergetic combination of patent translation and patent retrieval in a well-defined machine learning framework. The project is led by Stefan Riezler whose work focuses on statistical machine translation and machine learning for natural language processing. The Institute for Computational Linguistics (ICL) in Heidelberg (www.cl.uni-heidelberg.de) is a lively research environment and one of the largest centers for computational linguistics in Germany.

The ideal candidate will have a PhD or Master’s degree (for the Postdoc and PhD position, respectively) in computational linguistics, computer science, or a related field. A profound background in machine learning and statistics and excellent programming skills (C/C++/Python) are required.

The appointment is for 3 years, with a start date of May 1, 2012 or soon thereafter. Salary is paid according to the German TV-L 13 scale.

Applications should be in PDF format and include a statement of research interests, university transcripts, a CV, and the names of two references. Review of applications begins at once and will continue until the position is filled. Please send inquiries and applications to

Anke Sopka & Corinna Schwarz, Sekretariat ICL
sekretariat@cl.uni-heidelberg.de