Research Associate in Nonlinear Time-Series Classification (Unilever sponsored project)

Department of Automatic Control & Systems Engineering
The University of Sheffield

We are seeking to appoint a Research Associate to work on an industry-sponsored project on nonlinear signal processing. This post offers an outstanding opportunity to work with a multidisciplinary team of researchers from academia and industry. The project is to develop a novel feature extraction, modelling and classification system for nonlinear time-series. The position will incur regular travel to the Unilever Research Port Sunlight, Merseyside.

Applicants should have a good honours first degree and have, or be working towards a PhD in signal processing or a related area. The successful applicant will have experience in nonlinear system identification, statistical signal processing and pattern classification. Good IT skills including programming ability and experience in MATLAB computing environment is essential. Previous experience of processing real time-series data is highly desirable.

Applicants should also have good communication and interpersonal skills, and be able to work with researchers from the industry. Ability to meet deadlines and provide timely deliverables is essential.

This post is fixed-term until 30 June 2012.

Salary: £28,251 to £30,870 per annum (Grade 7)

Closing Date: 15th December 2011

It is anticipated that interviews and other selection action will be held on 20 December 2011.

For access to the full job advert, visit http://www.shef.ac.uk/jobs
(Job Reference Number: UOS003674).

Announcing the PASCAL Heart Sounds Challenge

We are pleased to announce the PASCAL-sponsored Heart Sounds Challenge. Here is your chance to prove your machine learning technique can outperform those of everyone else – and win an iPad for your efforts! (Also come to the Canary Islands to present your results in a workshop after AISTATS!)

For more details see: http://www.peterjbentley.com/heartchallenge/

According to the World Health Organisation, cardiovascular diseases (CVDs) are the number one cause of death globally: more people die annually from CVDs than from any other cause. An estimated 17.1 million people died from CVDs in 2004, representing 29% of all global deaths. Of these deaths, an estimated 7.2 million were due to coronary heart disease. Any method which can help to detect signs of heart disease could therefore have a significant impact on world health. This challenge is to produce methods to do exactly that. Specifically, we are interested in creating the first level of screening of cardiac pathologies both in a Hospital environment by a doctor (using a digital stethoscope) and at home by the patient (using a mobile device).

For this challenge we have two datasets comprising several hundred real heart sounds, gathered from an iphone app by the general public, and by a digital stethoscope in a noisy hospital environment.

Challenge 1 is segmentation – can your method correctly identify the “lub dub” (S1 and S2) components of the sound?

Challenge 2 is classification – can your method correctly classify the heart sounds into categories such as Normal, Murmur, Extra Heart Sound, and Artifact?

This problem is of particular interest to machine learning researchers as it involves classification of audio sample data, where distinguishing between classes of interest is non-trivial. Data is gathered in real-world situations and frequently contains background noise of every conceivable type. The differences between heart sounds corresponding to different heart symptoms can also be extremely subtle and challenging to separate. Success in classifying this form of data requires extremely robust classifiers. Despite its medical significance, to date this is a relatively unexplored application for machine learning.

Enquiries and submission, email: Yiqi Deng, y.deng.11(at)ucl.ac.uk

CFP: British Machine Vision Conference 2012

BMVC 2012: British Machine Vision Conference, University of Surrey, UK Sept 3-7th 2012

CALL FOR PARTICIPATION

http://bmvc2012.surrey.ac.uk/

The British Machine Vision Conference (BMVC) is one of the major international conferences on machine vision and related areas. Organized by the British Machine Vision Association, the 23rd BMVC will be held in Guildford UK, at the University of Surrey.

Authors are invited to submit full-length high-quality papers in image processing and machine vision. Papers covering theory and/or application areas of computer vision are invited for submission. Submitted papers will be refereed on their originality, presentation, empirical results, and quality of evaluation.

All papers will be reviewed *doubly blind*, normally by three members of our international programme committee. Please note that BMVC is a single track meeting with oral and poster presentations and will include two keynote presentations and two tutorials.

Topics include, but are not limited to:

• Statistics and machine learning for vision
• Stereo, calibration, geometric modelling and processing
• Person, face and gesture tracking
• Object and activity recognition
• Motion, flow and tracking
• Segmentation and feature extraction
• Model-based vision
• Image processing techniques and methods
• Texture, shape and colour
• Video analysis
• Document processing and recognition
• Vision for quality assurance, medical diagnosis, etc.
• Vision for visualization, interaction, and graphics

Conference Chairs:
Dr John Collomosse
Dr Krystian Mikolajczyk
Prof Richard Bowden

Important Dates
26 April 2012 Abstracts due
3 May 2012 Full paper submissions due
14 June 2012 Deadline for return of reviews
2 July 2012 Area chair recommendations due
6 July 2012 Author notifications
1 August 2012 Camera ready papers due
3-7 September 2012 Conference

See http://bmvc2012.surrey.ac.uk/ for more details

Two PhD Studentships at UCL Department of Statistical Science

PhD Studentships in Statistical Methodology and its Application (x2)

Vacancy Information
Applications are invited for two PhD funding opportunities based in the UCL Department of Statistical Science, available immediately.

The studentships are funded by UCL Impact awards held by Professor Mark Girolami in collaboration with Xerox Research Centre Europe. The awards are tenable for 36 months and cover tuition fees plus a stipend of £15,590 per annum (based on the standard UK Research Council rate with London weighting). The awards may be used to support UK and EU nationals only.

The research will be carried out in close collaboration with Dr Cedric Archambeau and Dr Guillaume Bouchard. The successful candidates will have the opportunity to visit Xerox Research Centre Europe on a regular basis.

Studentship Description
Advanced Monte Carlo Methods for Images and Text. The Bayesian framework for statistical inference is largely dependent on numerical simulation for all but the most straightforward of statistical models. In the probabilistic representation of digital documents comprised of texts, images and embedded information, sophisticated statistical models are often required. It is hugely challenging to perform simulation based inference over these classes of models due to a variety of factors such as (1) exceedingly high number of parameters in the model, (2) the discrete nature of the configuration space, (3) lack of strong likelihood-based identifiability and (4) strong posterior correlation of parameters. This project will seek to develop generic Monte Carlo sampling methods that address some of the issues listed above.

Probabilistic Models for Adaptive Content Creation. This research project will focus on the development of structured prediction models to build document templates and learn to customize texts or sentences according to user preferences and habits. Conditional language models to generate human readable text based on the specific target application and device appropriate algorithms for the generation of small pieces of text, such as introductory sentences will also be developed. This project will draw upon recent advances in Natural Language Processing tools, Machine Learning algorithms and Stochastic Optimization techniques, in developing intelligent document creation tools.

Person Specification
The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class BSc degree, or an MSc with merit or distinction in Mathematics, Statistics, Computer Science, or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable.

Eligibility
Candidates should apply for the MPhil/PhD in Statistical Science in the usual way by completing the online form and, in addition, send an up-to-date CV and covering letter directly to the Department making their case for the funding. The CV and covering letter should be sent to Dr Russell Evans at the email address below.

Please note that while the closing date given below refers to the general admissions deadline, applications will be considered on a rolling basis and the process will close as soon as both studentships are filled. You are therefore advised to apply as soon as possible.

Contact name
Dr Russell Evans

Contact details
russell(at)stats.ucl.ac.uk

Closing Date
3 Aug 2012

Studentship Start Date
No later than 30 September 2012

PhD position opening, University of Sheffield

A PhD position is available in the groups of Machine Learning & Computational Biology, Department of Computer Science, University of Sheffield, under the supervision of Dr Vasilaki. The successful candidate will work in the field of Spiking Neural Networks and Synaptic Plasticity.
This position is part of a larger European consortium, NAMASEN (http://www.namasen.net) with the participation of 12 academic and industrial partners. Details about the specific PhD position (Early Stage Researcher) can be found in the documentation of the NAMASEN web site. The position comes with very competitive salary and travel money, as well as the possibility of extended visits to the participating groups.

Starting Date: 1st May 2012.

Candidate Profile
Applicants should have the equivalent of a UK 1st class/2.1 degree or Masters in a relevant discipline (mathematics, physics, computer science or engineering). They should be well motivated to do a PhD in the field of Computational Neuroscience and to interact with the interdisciplinary, multinational NAMASEN consortium.

Eligibility criteria
At the start of their fellowship, researchers may not have resided or carried out their main activity (work, studies, etc.) in UK for more than 12 months, during the 3 years immediately prior to the start date of his/her appointment. In addition, candidates must NOT have worked in a research position of received research training for more than 4 years from the date obtained their undergraduate degree.

Why study at Sheffield?
The University of Sheffield is a member of the Russell Group of leading research-intensive universities. It was ranked 40th in the world’s top 100 universities by the Global University Ranking Study 2009, and is consistently ranked amongst the top 20 universities in the United Kingdom and Europe according to The Good University Guide. In 2011, QS World University Rankings placed Sheffield as the 72nd university worldwide and THE World University rankings as 101st. The university has produced five Nobel Prize winners so far.
The city of Sheffield is well connected with Manchester International Airport (1h15) and to London St Pancras (2h07). The Peak District, the first and largest of Britain’s National Parks, is just five miles from Sheffield city centre.

How to apply
Application process: Send by email to Dr Vasilaki a motivation letter (including your reasons for applying to this specific PhD position), a full CV, copies of transcripts and 2 to 3 names of references. Candidates that match the required profile will be continuously interviewed until the position is filled.

— Dr Eleni Vasilaki, Lecturer
Web: www.dcs.shef.ac.uk/~eleni

Biometric School 2012 (Winter Edition) — a PASCAL2 sponsored event

An event co-sponsored by PASCAL2, IEEE and IAPR.

CALL FOR PARTICIPATION

*** Registration deadline is 23 Dec 2011 ***

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EXCLUSIVE TO PASCAL PHD STUDENT MEMBERS
As a gesture of gratitude to PASCAL2 sponsorship to the event, we are offering a special registration rate of EUR 450 (with full board accommodation) to Pascal PhD students
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Objective: To expose the latest developments and technology behind the most commonly used biometrics (face, fingerprint, and iris recognition) in automatic person recognition, multimodal biometrics, and biometric cryptosystems.

Audience: Engineers and scientists; post-graduate students and academic staff; entrepreneurs, project managers, and government officers who need an in-depth understanding of the mechanisms behind the technology.

(Audience size is limited to 50)

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REGISTRATION

Register by 23 December 2011:
RM 2750 (student) / RM3250 (non-student)
Registration includes accommodation, meals, and social event

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TOPICS AND SPEAKERS

* Fingerprint recognition: state of the art and novel developments
Prof. Davide Maltoni (Italy)

* Touchless fingerprint recognition & Biometrics and privacy protection
Prof. Jaihie Kim (Korea)

* Iris recognition: state of the art, issues & challenges
Prof. Tieniu Tan (China)

* Face recognition in challenging conditions, facial pre-processing, learning & inference
Dr. Simon Prince (UK)

* Linear and advanced classifier design for biometric systems
Prof. Kar-ann Toh (Korea)

* Fusion strategies and template security in biometric systems
Dr. Karthik Nandakumar (Singapore)

* Novel research directions in multimodal biometrics: Incorporating quality, cohort and client-dependent information
Dr. Norman Poh (UK)

* Biometric-key computation
Dr. Andrew Teoh (Korea)

————————————————————————–
IMPORTANT DATES
Early registration and abstract submission deadline : 31 October 2011
Late registration deadline : 23 December 2011
School : 9-13 January 2012

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MORE INFORMATION
Visit http://www.biometricschool.org or email info@biometricschool.org

Research Associate/Research Fellow – Natural Speech Technology

Fixed-term for 3 years

Salary:
Research Associate – Grade 7: £28,251 to £35,788 per annum
Research Fellow – Grade 8: £36,862 to £44,016 per annum

Closing Date
6 December 2011

The Speech and Hearing research group in the Department of Computer Science (SPandH) is a partner in the EPSRC Programme Grant in Natural Speech Technology (NST), in collaboration with the Universities of Edinburgh and Cambridge. NST is a large and ambitious project, aiming to significantly advance the state-of-the-art in speech technology by making it more natural, approaching human levels of reliability, adaptability and conversational richness. The total duration of the NST programme is 5 years and it is organised in themes that cover a diverse set of collaborative studies in speech recognition and synthesis. Applications, practical demonstrations and interaction with technology users in industry are also part of the programme. The successful applicant will work on speech recognition research topics under the NST programme at Sheffield.

SPandH has developed state-of-the-art automatic speech recognition systems that have repeatedly shown best performance in international competitions (U.S. NIST) and are publicly available (www.webasr.org). In clinical applications, SPandH has introduced a user-driven methodology for personalised speech technology. Together, these advances form the foundation for Sheffield work within NST. Excellent computing resources are available to allow ambitious experiments with innovative ideas. This is an opportunity to work in a well-connected international team with world-leading reputations in speech recognition research and in collaboration with outstanding groups at the Centre for Speech Technology Research at Edinburgh and the Machine Intelligence Lab at Cambridge University.

Applicants should have a PhD (or have equivalent experience) in a related subject area. Applicants are required to have a good track record in research of speech recognition and/or machine learning topics. Experience in one or more of the following areas will be an advantage:

statistical machine learning ,
pattern processing
signal processing
acoustic or language modelling for automatic speech recognition

Solid knowledge of Unix type operating systems and programming in C/C++ is required. For an appointment at Research Fellow level, experience in research management is essential as candidates are expected to take a leading role in site scientific management.

For further information see
http://www.jobs.ac.uk/job/ADM425/research-associate-research-fellow

For informal enquiries please contact Thomas Hain (t.hain(at)dcs.shef.ac.uk) or Phil Green (p.green(at)dcs.shef.ac.uk).

Research Associate in Nonlinear Time-Series Classification

Department of Automatic Control & Systems Engineering
The University of Sheffield

We are seeking to appoint a Research Associate to work on an industry-sponsored project on nonlinear signal processing. This post offers an outstanding opportunity to work with a multidisciplinary team of researchers from academia and industry. The project is to develop a novel feature extraction, modelling and classification system for nonlinear time-series. The position will incur regular travel to the Unilever Research Port Sunlight, Merseyside.

Applicants should have a good honours first degree and have, or be working towards a PhD in signal processing or a related area. The successful applicant will have experience in nonlinear system identification, statistical signal processing and pattern classification. Good IT skills including programming ability and experience in MATLAB computing environment is essential. Previous experience of processing real time-series data is highly desirable.

Applicants should also have good communication and interpersonal skills, and be able to work with researchers from the industry. Ability to meet deadlines and provide timely deliverables is essential.

This post is fixed-term until 30 June 2012.

Contract Type: Fixed-term with an end date of 30 June 2012
Salary: £28,251 to £30,870 per annum (Grade 7)

Closing Date: 28th November 2011

For access to the full job advert, visit http://www.shef.ac.uk/jobs
(Job Reference Number: UOS003589).

Call for Participation: Relevance Prediction Challenge / WSDM 2012 – Web Search Click Data Workshop

We are pleased to announce the launch of the Relevance Prediction Challenge, which is a part of the WSDM 2012 Web Search Click Data (WSCD) workshop. This challenge provides a unique opportunity to consolidate and scrutinize the work from industrial labs on predicting the relevance of URLs using user search behavior. It provides a fully anonymized dataset shared by Yandex, which has user queries, clicks on URLs and their relevance labels.

Important Dates:
Oct 15, 2011 – Challenge opens
Dec 15, 2011 – End of challenge
Dec 25, 2011 – Winners candidacy notification
Jan 20, 2012 – Reports deadline
Feb 12, 2012 – WSCD workshop at WSDM 2012 (http://research.microsoft.com/en-us/um/people/nickcr/wscd2012/) and Winners announcement

The top three competitors will receive the following cash prizes:

1st place: $5,000
2nd place: $3,000
3rd place: $1,000

Contestants can participate as individuals, or as teams. You will find a detailed description of the task, data sets and result evaluation methodology at:

http://imat-relpred.yandex.ru/en

Challenge Organizers

Pavel Serdyukov, Eugene Kharitonov, Alexey Gorodilov (Yandex)
Georges Dupret (Yahoo!)
Nick Craswell (Microsoft)

Post-doc positions available at the University of Trento (linguistics, machine learning, cognitive science)

3 (RENEWABLE) 2-YEAR POST-DOC POSITIONS AVAILABLE

The CIMeC-CLIC laboratory of the University of Trento, an
interdisciplinary group of researchers studying language and
conceptualization using both computational and cognitive methods
(clic.cimec.unitn.it) announces the availability of at least 3
(renewable) 2-year Post-Doc positions.

The scholarships are funded by a 5-year European Research Council
Starting Grant awarded to the COMPOSES (COMPositional Operations in
SEmantic SPACE) project (clic.cimec.unitn.it/composes), that
aims at modeling composition in distributional semantics. The project
is expected to have strong impact on both theoretical and
computational semantics, as well as their cognitive underpinnings.

* Desired Profiles *

Given the interdisciplinary nature of the project, we seek brilliant
researchers with any of the following backgrounds:

– Machine learning (areas of special interest: regression,
regularization methods, hierarchical regression, autoencoders,
curriculum learning, scaling machine learning to large multivariate
and multi-level problems, dealing with very sparse data);

– Psycholinguistics, experimental linguistics or cognitive science
(areas of special interest: systematic judgment elicitation methods
such as Likert scales or magnitude estimation, crowdsourcing,
semantic processing);

– Formal and/or computational semantics (areas of special interest:
Montague Grammar and its derivatives, distributional semantics)

Advanced programming and mathematical skills are required of
candidates from machine learning. Linguists and cognitive scientists
must possess at least basic programming skills and a reasonable
knowledge of statistics.

If you think that your background is relevant to the research program
outlined on the project website (clic.cimec.unitn.it/composes) and you
have good programming and quantitative skills, please do get in touch
even if you do not fit any of the profiles above.

All researchers are expected to have an interest in working in an
interdisciplinary environment.

* The Research Environment *

The CLIC lab (clic.cimec.unitn.it) is a unit of the University of
Trento’s Center for Mind/Brain Sciences (CIMeC,
http://www.unitn.it/en/cimec), an English-speaking, interdisciplinary
center for research in brain and cognition whose staff includes
neuroscientists, psychologists, (computational) linguists, computer
scientists and physicists.

CLIC consists of researchers from the Departments of Computer Science
(DISI) and Cognitive Science (DISCoF) carrying out research on a range
of topics including concept acquisition, corpus-based computational
semantics, combining NLP and computer vision, combining brain and
corpus data to study cognition, formal semantics and theoretical
linguistics. Modeling composition in distributional semantics is
increasingly a focus point of CLIC, and activity in this area will
grow considerably thanks to COMPOSES funds.

CLIC is part of the larger network of research labs focusing on
Natural Language Processing and related domains in the Trento region,
that is quickly becoming one of the areas with the highest
concentration of researchers in NLP and related fields anywhere in
Europe.

The CLIC/CIMeC laboratories are located in beautiful Rovereto, a
lively town in the middle of the Alps, famous for its contemporary art
museum, the quality of its wine, and the range of outdoors sport and
relax opportunities it offers:

http://en.wikipedia.org/wiki/Rovereto

* Application Information *

For further information, please send an expression of interest to
marco.baroni(at)unitn.it, attaching a CV. Positions are available
immediately and open until filled.