Researcher position, Statistical Machine Translation – Xerox Grenoble, France

Researcher, 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.

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 (www.translectures.eu).

Required experience and qualifications:

– PhD in computer science or computational linguistics, with focus on
statistical methods
– Experience with Statistical Machine Translation.
– 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: July 2013

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-int.xrce.xerox.com/About-XRCE/Career-opportunities/Researcher-Statistical-Machine-Translation

Second Call for papers SISAP 2013

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SISAP 2013 – SECOND CALL FOR PAPERS
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We apologize if you receive duplicates of this CFP.
Please feel free to distribute it to those who might be interested.

SISAP 2013: 6th International Conference on Similarity Search and Applications
October 2-4, 2013
A Coruna, Spain

Web site: http://www.sisap.org/2013
Facebook: http://www.facebook.com/sisap2013
Twitter: http://twitter.com/sisap2013,

Scope

The International Conference on Similarity Search and Applications (SISAP) is an annual forum for researchers and application developers in the area of similarity data management. It aims at the technological problems shared by numerous application domains, such as data mining, information retrieval, computer vision, pattern recognition, computational biology, geography, biometrics, machine learning, and many others that need similarity searching as a necessary supporting service.

The SISAP initiative (www.sisap.org) aims to become a forum to exchange real-world, challenging and innovative examples of applications, new indexing techniques, common test-beds and benchmarks, source code and up-to-date literature through its web page, serving the similarity search community. Traditionally, SISAP puts emphasis on the distance-based searching, but in general the conference concerns both the effectiveness and efficiency aspects of any similarity search problem.

The series started in 2008 as a workshop and has developed over the years into an international conference with Lecture Notes in Computer Science (LNCS) proceedings. As in previous editions, a small selection of the best papers presented at the conference will be recommended for inclusion in a special issue of Information Systems. In October 2013, SISAP will take place in A Coruna, Spain.

Keynote Speakers

Ricardo Baeza-Yates (VP of Yahoo! Research for Europe, Middle East and Latin America)
Jiri Matas (Center for Machine Perception, Czech Technical University)

Topics of interest

The specific topics include, but are not limited to:
Similarity queries – k-NN, range, reverse NN, top-k, etc.
Similarity operations – joins, ranking, classification, categorization, filtering, etc.
Evaluation techniques for similarity queries and operations
Merging/combining multiple similarity modalities
Cost models and analysis for similarity data processing
Scalability issues and high-performance similarity data management
Feature extraction for similarity-based data findability
Test collections and benchmarks
Performance studies, benchmarks, and comparisons
Similarity Search over outsourced data repositories
Similarity search cloud services
Languages for similarity databases
New modes of similarity for complex data understanding
Applications of similarity-based operations
Image, video, voice, and music (multimedia) retrieval systems
Similarity for forensics and security

Important Dates

Abstract submission: May, 3rd, 2013
Paper submission: May, 10th, 2013
Notification: June, 21st, 2013
Final version: July, 10th, 2013
Conference: October 2-4, 2013

Submission guidelines

Papers submitted to SISAP 2013 must be written in English and formatted according to the LNCS guidelines. Full papers can be up to 12 pages, while short papers, case-studies/applications, and demos can be up to 6 pages (read below for types of contribution). By submitting a paper, its authors commit to have the paper presented at the conference by at least one of them if the paper is accepted.

Contributions

Authors are invited to submit previously unpublished papers on their research in the area of similarity search and applications. Papers should present original research contributions which bring out the importance of algorithms to applications. SISAP submissions can be of three kinds:

Research papers (full and short): SISAP accepts both full (12 pages) and short papers (6 pages). The full papers are expected to be descriptions of complete technical work, while the short papers will describe interesting, innovative ideas, which nevertheless require more work to mature – vision papers should also be submitted as short papers. All papers, regardless of size, will be given an entry in the conference proceedings.

Case-studies and applications: Submissions describe applications of existing similarity search technologies to interesting problems, including a description of the encountered challenges, how they were overcome, and the lessons learned. All papers on this track will be given an entry in the conference proceedings and a presentation slot, though the presentation slot duration may be shorter than for full research papers.

Demonstration papers: Submissions should provide the motivation for the demonstrated concepts, the information about the technology and the system to be demonstrated (including a system description, functionality and figures when applicable), and should state the significance of the contribution. Evaluation criteria for the demonstration proposals include: the novelty, the technical advances and challenges, and the overall practical attractiveness of the demonstrated system. Demonstration papers will also be given an entry in the conference proceedings – online demos are expected at the conference.

Program comittee chairs

Pavel Zezula, Masaryk University, Czech Republic
Nieves R. Brisaboa, Universidade da Coruna, Spain

Program comittee members

Giuseppe Amato, ISTI – Istituto di Scienza e Tecnologia dell’Informazione, Italy
Laurent Amsaleg, IRISA – Institut de Recherche en Informatique et Systemes Aleatoires, France
Benjamin Bustos, Universidad de Chile, Chile
Edgar Chavez, Universidad Michoacana, Mexico
Paolo Ciaccia, University of Bologna, Italy
Richard Connor, Strathclyde University, UK
Andrea Esuli, Istituto di Scienza e Tecnologie dell’Informazione, Italy
Rosalba Giugno, University of Catania, Italy
Michael Houle, National Institute of Informatics, Japan
Alexis Joly, INRIA, France
Daniel Keim, Universitat Konstanz, Germany
Eamonn Keogh, University of California – Riverside, USA
Magnus Lie Hetland, Norwegian University of Science and Technology, Norway
Yannis Manolopoulos, Aristotle University of Thessaloniki, Greece
Rui Mao, Shenzhen University, China
Luisa Mico, University of Alicante, Spain
Henning Muller, University of Applied Sciences Western Switzerland, Switzerland
Gonzalo Navarro, Universidad de Chile, Chile
Arlindo Oliveira, Lisbon Technical University, Portugal
Jose Oncina, University of Alicante, Spain
Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece
Marco Patella, University of Bologna, Italy
Vladimir Pestov, University of Ottawa, Canada
Matthias Renz, Ludwig-Maximilians-Universitat Munchen, Germany
Hanan Samet, University of Maryland, USA
Tomas Skopal, Charles University in Prague, Czech Republic
Bjorn Thor Jonsson, Reykjavik University, Iceland

Local organization

Nieves R. Brisaboa, Universidade da Coruna, Spain
Oscar Pedreira, Universidade da Coruna, Spain

PASCAL2 IASD challenge: Deadline extension

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Interactive Annotation of Sequential Data (IASD)
PASCAL2 challenge
http://translectures.eu/iasd
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— Please, accept our apologies in case of multiple receptions —

*** New First Phase Deadline: March 25 ***

Dear colleagues,

We are pleased to announce an extension of the Interactive Annotation
of Sequential Data (IASD) PASCAL2 challenge deadlines. The aim of the
IASD challenge is to explore innovative, cost-effective solutions for
generating accurate transcriptions for video lectures from
VideoLectures.NET, and, at a more general level, speech-like
sequential data. The focus is not on developing advanced speech
recognition techniques, so much as on the study of techniques for
interacting intelligently with users. These techniques should seek to
optimise the trade-off between user effort and accuracy, in such a way
that the winning approaches are those reaching the top-3 accuracy with
minimum feedback.

Important Dates (extended):

Feb 12, 2013 – First challenge phase starts.
Mar 25, 2013 – First phase ends: selection of the 3 best systems.
Mar 26, 2013 – Second phase starts (for the 3 best systems only).
Apr 5, 2013 – Second phase ends: winners annouced and ranked.
Apr 6, 2013 – Reports deadline for the winners.

Presentation and awards:

The winners will be invited to present their systems at the joint
EUCOGIII/PASCAL meeting in Palma de Mallorca on April 11, 2013, with
travel costs covered by the meeting organisation. Winners attending
the meeting will be awarded with the following net prizes (after
taxes):

1st prize: €1000
2nd prize: €600
3rd prize: €300

You will find a detailed description of the challenge, data and
evaluation methodology at:

http://translectures.eu/iasd

Challenge Organizers:

Nicolas Serrano, Jesus Andres, Alfons Juan (UPV)
John Shawe-Taylor, Davor Orlic (K4A)
Mitja Jermol (JSI)

Challenge sponsors:

PASCAL2 Network (http://www.pascal-network.org)
EUCOGIII Network (http://www.eucognition.org)
transLectures project (http://translectures.eu)
Universitat Politecnica de Valencia (http://www.upv.es/index-en.html)
Knowledge for all (http://www.k4all.org)
Jožef Stefan Institute (http://www.ijs.si/ijsw/JSI)

PhD studentship in computer vision, eye-tracking, and natural language, University of Edinburgh

University of Edinburgh
School of Informatics

PHD STUDENTSHIP IN COMPUTER VISION, EYE-TRACKING, AND NATURAL LANGUAGE

Applications are invited for a fully funded, three-year PhD studentship that combines ideas from computer vision, eye-tracking research, and natural language processing. The aim of the PhD project is to develop techniques for using human fixation data as recorded by an eye-tracker to train computer vision models, thus reducing the need for manual annotation. This approach will be augmented to exploit textual data (e.g., image captions) to improve object labeling. Another project aim is to crowd-source the eye-tracking data, e.g., through the use of webcams.

Applicants for the studentship must have:

* Strong undergraduate degree in computer science or a related
discipline

* Excellent programming skills

* Solid mathematical foundations (especially linear algebra and
probability theory)

* Fluency in English, both written and spoken

* UK or EU nationality — this is mandatory; applicants of other
nationalities will not be considered

* Master’s degree in a relevant area is desirable

* Experience in computer vision, machine learning, natural
language processing, or eye-tracking is desirable

The School of Informatics at Edinburgh is one of the top-ranked computer science departments in Europe and offers an exciting, interdisciplinary research environment. Edinburgh is a beautiful historic city with a high quality of life.

Starting date: September 2013 as soon as possible after that.

The PhD work will be carried out under the supervision of Dr. Vittorio Ferrari and Dr. Frank Keller, whose research interests can be found at:

http://groups.inf.ed.ac.uk/calvin/
http://homepages.inf.ed.ac.uk/keller/

For pre-screening, please send applications to the email address below, including:

* Full CV

* Full transcripts of both undergraduate and Master’s degree (if
applicable); this studentship requires high grades, especially
in mathematics and programming courses

* The names and email addresses of two referees

* List of publications, if you have prior research experience

Contact: Vittorio Ferrari, vferrari@staffmail.ed.ac.uk

Deadline: 25 April 2013

BioASQ challenge on large-scale biomedical semantic indexing and question answering

BioASQ challenge on large-scale biomedical semantic indexing and question
answering
(BioASQ workshop to be collocated with CLEF 2013 in Valencia, Spain on
September 27, 2013)

Web site: http://bioasq.org/
twitter: https://twitter.com/bioasq

The BioASQ challenge aims to push for a solution to the information access
problem of biomedical experts. It will evaluate the ability of systems to
perform various tasks in the biomedical QA process:
1. large-scale classification of biomedical documents onto ontology concepts
(semantic indexing),
2. classification of biomedical questions onto relevant concepts,
3. retrieval of relevant document snippets, concepts and knowledge base
triples, and
4. delivery of the retrieved information in a concise and
user-understandable form.

In particular, the challenge will comprise two main tasks:

Task 1a. Large-scale online biomedical semantic indexing
Large-scale semantic indexing will be evaluated on the whole of PubMed. In
particular, participants will be asked to classify incoming documents before
the human curators do:
* BioASQ will distribute new unclassified PubMed documents.
* Participants will have a limited response time to attach MeSH terms.

Task 1a will run for three consecutive periods (batches) of 6 weeks each.
The first batch will start on April 15, 2013. Separate winners will be
announced for each batch. Participation in the task can be partial, i.e. in
some of the batches.

Task 1b. Biomedical semantic QA
The systems will be evaluated against gold answers created by a team of
biomedical experts. The task will run in two phases:
In Phase A:
* BioASQ will transmit simultaneously questions from the benchmark.
* Participants will have limited time to respond with concepts, snippets,
triples.
In Phase B:
* BioASQ will distribute questions + concepts, snippets, triples.
* Participants will respond with facts, summaries, etc.

The two phases will run consecutively, i.e. Phase B will start after the end
of phase A. Phase A will start on June 3, 2013. Separate winners will be
announced for each phase and each task target, i.e. concepts, snippets,
triples, etc. Participation in the task can be partial, i.e. in one of the
two phases and only for some of the targets.

Prizes will be awarded to the winners. Details about the prizes will be
announced on the Web site of the challenge.

Important dates:
March 18, 2013: Training data available for task 1a. Dry-run data available
for task 1b.
April 15, 2013: Start of task 1a.
June 3, 2013: Start of task 1b.
July 15, 2013: Submission of papers for BioASQ workshop
August 30, 2013: End of challenge
September 27, 2013: BioASQ workshop, collocated with CLEF 2013

The BioASQ challenge and workshop are organised by the BioASQ project,
supported by the European Commission within the 7th Framework Programme
(Grant Agreement No. 318652).

CFP: ICML 2013 Workshop on Spectral Learning

Call for Papers: Workshop on Spectral Learning — @ICML2013
June 20 or 21, Atlanta (GA), USA
Website: http://sites.google.com/site/spectrallearningworkshop/

Many problems in machine learning involve collecting high-dimensional multivariate observations or sequences of observations, and then fitting a compact model which explains these observations. Recently, linear algebra techniques have given a fundamentally different perspective on how to fit and perform inference in these models. Exploiting the underlying spectral properties of the model parameters has led to fast, provably consistent methods for parameter learning that stand in contrast to previous approaches, such as Expectation Maximization, which suffer from slow convergence and issues related to local optima.

In the past several years, these spectral learning algorithms have become increasingly popular. They have been applied to learn the structure and parameters of many models including predictive state representations, finite state transducers, hidden Markov models, latent trees, latent junction trees, probabilistic context free grammars, and mixture/admixture models. Spectral learning algorithms have also been applied to a wide range of application domains including system identification, video modeling, speech modeling, robotics, and natural language processing.

The focus of this workshop will be on spectral learning algorithms, broadly construed as any method that fits a model by way of a spectral decomposition of moments of (features of) observations. We would like the workshop to be as inclusive as possible and encourage paper submissions and participation from a wide range of research related to this focus. This includes (but is not limited to):

* Linear-algebraic methods for estimation and inference in probabilistic models and weighted automata/operator models
* Spectral approaches to dimension reduction (e.g., with applications in estimating mixture models)
* Method-of-moment estimation via higher-order tensor decompositions
* Spectral graph theory and applications in clustering and learning on manifolds
* Domain-specific aspects of using spectral approaches in applications

Submitted papers should be in the ICML 2013 format with a maximum of 4 pages (not including references). Please e-mail your submission to spectralicml2013@gmail.com with the subject line “Submission to Spectral Learning Workshop”. Contributions will be considered for both short talks and poster presentations.

Concurrent submissions to the workshop and the main conference (or other conferences) are permitted.

Important dates:

* Submission deadline: April 6, 2013
* Notification of acceptance: April 20, 2013 (tentative)
* Workshop: June 20 or 21, 2013

Organizers:

* Byron Boots (University of Washington)
* Daniel Hsu (Microsoft Research New England)
* Borja Balle (Universitat Politècnica de Catalunya)
* Ankur Parikh (Carnegie Mellon University)

PostDoc in Dimensionality Reduction and Information Visualization at UCLouvain

The Université catholique de Louvain invites applications for a 2-year postdoctoral position in Machine Learning/Information Visualisation, beginning July 1, 2013.

The visual interpretation of data is an essential step to guide any further processing or decision making. Data visualization is tackled independently from two different angles in the scientific community. The domain of machine learning addresses mainly statistical, mathematical, and algorithmic aspects with dimensionality reduction (DR) techniques, whereas the field of information visualization focuses on the interaction with the user (man-computer interface, visual efficacy, user-friendliness). In this context, the project aims at bridging the two approaches. More specifically, the project intends to import the concepts of interactivity and controllability from the field of information visualization and to integrate them in advanced DR techniques in order to improve their acceptance by users and broaden their range of application.

The successful applicant will hold a Ph.D. degree delivered not earlier than July 1, 2007, and must not have the Belgian nationality. In addition, the candidate must not have lived in Belgium for more than 2 years since July 1, 2010. The net salary after deduction of taxes and social security is about 26000 euros per year, or more, depending on seniority.

Applicants should be knowledgeable in machine learning, data mining, information visualisation, with a particular interest in manifold learning, dimensionality reduction and information retrieval. Applicants should have a strong background in computer science and applied mathematics.

A working knowledge of English language is mandatory. French is an optional asset.

Working location will be Louvain-la-Neuve, a lively pedestrian town in the suburbs of Brussels, where most of the Université catholique de Louvain is located. The work will be carried out in collaboration with Profs. John A. Lee
(http://scholar.google.be/citations?user=ZopTupcAAAAJ) and Michel Verleysen (http://perso.uclouvain.be/michel.verleysen/), in the ICTEAM institute (http://www.uclouvain.be/en-icteam.html).

Application procedure: Interested individuals should send a CV, a brief statement of research and development interests (max. 1 page), and the names and contact details of two references by e-mail to John Lee
(john.lee@uclouvain.be) with subject “Postdoc DRedVis”.

Candidates interested should send their application before March 15th, 2013; we reserve the right to accept late applications. The position will be available for an initial period of 1 year, with possible one-year extension after mid-term evaluation.

Postdoctoral Position in Machine Learning at INRIA Lille – Team SequeL

Applications are invited for a Postdoctoral position on the general area of “Sequential Decision-making in Online Marketing” at INRIA Lille – Team SequeL. Below is the detail of this call.

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Title: Sequential Decision-making in Online Marketing: Optimizing the Lifetime Value of Customers

Keywords: sequential decision-making, reinforcement learning, online marketing and advertising, exploration/exploitation dilemma, bandit algorithms, adaptive resource allocation, regret minimization, optimization

Research Program:

The candidate is expected to conduct research on both theoretical and applied aspects of the problem of “Sequential Decision-making in Online Marketing” and related problems (see the description below), collaborate with researchers and Ph.D. students at INRIA and outside, and publish the results of her/his research in conferences and journals. The candidate will work with Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza) and other researchers at Team SequeL (https://sequel.lille.inria.fr).

With the growth of online marketing, customers visit websites on a regular basis (sometimes daily in the case of banking, e-commerce, and media websites), and at each visit a stream of interactions occurs between the company (promotion, advertisement, email) and a customer (purchase, click on an ad, signing up for a newsletter). This creates many opportunities for a company to reach a customer and make decisions to optimize its objective function (revenue, customer satisfaction, etc.). Today, these marketing decisions are mainly made in a myopic way (mainly using contextual bandit algorithms) without taking into account the lifetime value of the customer. This myopic approach assumes that a decision made by the company does not affect the customer’s future interactions with the company. However, in many applications the sequential nature of the problem is significant (having a long-term relation with customers is important for the company), and thus, myopic decisions have po!
or performance in these problems. This creates an opportunity for reinforcement learning (RL) techniques to play a significant role in this emerging field.

The objective of this research program is to answer fundamental questions related to the use of myopic (contextual bandit algorithms) and non-myopic (sequential decision-making and RL algorithms) decision-making methods in the growing field of online marketing. Questions such as

– Feature selection and dealing with high dimensional data: discovering the right representation for the problem at hand and dealing with the size and dimensionality of the data are among the most important questions in these applications. The size and dimensionality of the data create difficulties for the standard sequential decision-making algorithms. This is closely related to another growing research direction: sequential decision-making with big data.

– Off-policy evaluation: how to evaluate a policy learned from a batch of historical data generated with a different policy (usually the company’s policy) with minimum interaction with the real-world environment. Running a strategy on the real system can be costly: it usually takes a long time to have a reasonable evaluation of its quality and more importantly is the risk of a big loss in case the strategy is not good.

– Discovering patterns in the sales funnel in order to find strategies to direct more customers through the funnel to the final sale.

– Dealing with the non-stationarity, mainly caused by change in the preferences of the customer, arrival and departure of customers, evolution of webpage contents, etc., and delayed feedback (significant delay between an action taken by the marketer and its effects on the customer) in the online marketing applications.

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Requirements:

The applicant will have a Ph.D. degree (by the starting date of the postdoctoral position) in Computer Science, Statistics, or related fields, with background in reinforcement learning, bandit algorithms, statistics, and optimization. Programming skills will be considered as a plus. The working language of the group is English, so the candidate is expected to have good communication skills in English.

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About INRIA and Team SequeL:

SequeL (https://sequel.lille.inria.fr) is one of the most dynamic teams at INRIA (http://www.inria.fr), with over 25 researchers and Ph.D. students working on several aspects of machine learning from theory to application, including statistical learning, reinforcement learning, and sequential decision-making. The SequeL team is involved in national and European research projects and has collaboration with international research groups. This allows the postdoctoral candidate to collaborate with leading researchers in the field at top universities in Europe and North America such as University College of London (UCL), University of Alberta, and McGill University. Moreover, in this project there is the possibility of close collaboration with an online marketing company in the US. Lille is the capital of the north of France, a metropolis with over one million inhabitants, and with excellent train connection to Brussels (30min), Paris (1h) and London (1h30).

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Benefits:

– Duration: 16 months – starting date of the contract : November 1, 2013
– Salary: 2620.84 Euros gross/month monthly salary
– Monthly salary after taxes: around 2138 Euros (medical insurance included)
– Possibility of French courses
– Help for housing
– Participation for transportation
– Scientific Resident card and help for husband/wife visa

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Application Submission:

The application should include a brief description of the applicant’s research interests and past experience, plus a CV that contains her/his degrees, GPAs, relevant publications, name and contact information of up to three references, and other relevant documents. Please send your application to mohammad.ghavamzadeh@inria.fr. The deadline for the application is April 15 but the applicants are encouraged to submit their application as soon as possible.

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This call has also been posted on

1) my webpage at

http://chercheurs.lille.inria.fr/~ghavamza/postdoc-ad-2013.html

2) the INRIA website at:

http://www.inria.fr/institut/recrutement-metiers/offres/post-doctorat/campagne-2013/%28view%29/details.html?id=PGTFK026203F3VBQB6G68LONZ&LOV5=4508&LG=FR&Resultsperpage=20&nPostingID=7352&nPostingTargetID=12788&option=52&sort=DESC&nDepartmentID=19

PhD Studentship in statistical machine translation – Xerox & University of Grenoble

PhD student, Statistical Machine Translation in Grenoble (France)

The Machine Learning for Document Access and Translation group of the Xerox Research Centre Europe (XRCE) conducts research in Statistical Machine Translation and Information Retrieval, Categorization and Clustering using advanced machine learning methods.

We are opening a position for a PhD studentship in the field of Statistical Machine Translation. The ideal candidate has a strong undergraduate curriculum in SMT, or otherwise in statistical NLP, and is passionate about devising, implementing and evaluating effective solutions to challenging language problems.

This PhD will be co-supervised with the Laboratory of Informatics of Grenoble (LIG) which conducts research in Machine Translation and Natural Language Processing. The candidate will register as a graduate PhD student at University of Grenoble.

Required experience and qualifications:

– Master-level degree in computer science, mathematics, statistics, or computational linguistics with focus on Machine Translation, statistical NLP, or machine Learning.
– A good command of English, as well as open-mindedness and the will to collaborate within a team.

Additional desirable features:

– A publication record
– Evidence of implementing systems.

Preferred starting date: ASAP

Application instructions

Inquiries can be sent to
Nicola.Cancedda at xrce.xerox.com
Laurent.Besacier at imag.fr

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

Postdoctoral Research Fellow on “Low-complexity source separation algorithms”, CVSSP, University of Surrey, U.K. (Closing on March 17th, 2013)

Research Fellow

Low-complexity source separation algorithms

Centre for Vision Speech and Signal Processing (CVSSP)
University of Surrey, United Kingdom
Salary: £29,541-£30,424 per annum
(Subject to qualifications and experience)
Applications are invited for a three-year postdoctoral research fellow position available at CVSSP, starting on Monday, April 1, 2013, to work on a project entitled “Signal Processing Solutions for a Networked Battlespace”, funded by the Engineering and Physical Sciences Research Council (EPSRC) and Defence Science and Technology Laboratory (Dstl), as part of the Ministry of Defence (MoD) University Defence Research Centre (UDRC) Scheme in signal processing. This project will be undertaken by a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff (LSSC) Universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech and Steepest Ascent. The overall aim of the project is to provide fundamental signal processing solutions to enable intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms, in order to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). The research fellow will be expected to work in close collaboration with our academic and industrial partners together with members of the lead consortium based at Edinburgh and Heriott Watt Universities.
The prospective research fellow will be expected to develop low-complexity robust algorithms for underdetermined, convolutive signal separation, broadband distributed beamforming. The work will be facilitated by low-rank and sparse representations, and directed toward fast implementations. He/she will develop robust source separation algorithms in highly dense signal environments, with the presence of uncertainties, such as weak signals and the unknown number of targets.
Successful applicants will join the CVSSP, a leading research group in sensory (visual and auditory) data analysis and interpretation, and will work closely with Dr Wenwu Wang, Prof Josef Kittler and Dr Philip Jackson. CVSSP is one of the largest UK research groups in machine vision and audition with more than 120 researchers, with core expertise in Signal Processing, Image and Video Processing, Pattern Recognition, Computer Vision, Machine Learning and Artificial Intelligence, Computer Graphics and Human Computer Interaction. CVSSP forms part of the Department of Electronic Engineering, which received one of the highest ratings (joint second position across the UK) in the last research quality assessment, i.e. 2008 RAE, with 70% of its research classified as either 4* (“world-leading”) or 3* (“internationally excellent”).

Applicants should have a PhD degree or equivalent in electrical and electronic engineering, computer science, mathematical science, statistics, physics, or related disciplines. Applicants should be able to demonstrate excellent mathematical, analytical and computer programming skills. Advantages will be given to the applicants who have experience in sparse representations, blind source separation, low-rank linear algebra, and/or machine learning.
For informal inquiries about the position, please contact Dr Wenwu Wang (w.wang@surrey.ac.uk).
For an application pack and to apply on-line please go to our website: http://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 683419 or email: k.li@surrey.ac.uk
The closing date for applications is Sunday March 17th, 2013.

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

We acknowledge, understand and embrace cultural diversity.