Postdoctoral Research Fellow (three-year fixed term) on “Statistical Anomaly Detection”, CVSSP, University of Surrey, U.K.

Research Fellow
Statistical anomaly detection

Centre for Vision Speech and Signal Processing (CVSSP)
University of Surrey, United Kingdom
Salary: £29,541-£31,331 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 algorithms and systems for automated statistical anomaly detection and classification in high dimensions for the networked battlespace. In particular, he/she will develop algorithms for automatic detection of anomalies from multidimensional, undersampled, non-complete datasets and unreliable sources, and solutions to anomaly detection with the presence of uncertainties and in complex networks (graphs), e.g., using domain knowledge.

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 anomaly detection, statistics, machine learning, signal processing, and/or pattern recognition.
For informal inquiries about the position, please contact Dr Wenwu Wang ( or Prof Josef Kittler (
For an application pack and to apply on-line please go to our website: If you are unable to apply on-line please contact Mr Peter Li, HR Assistant on Tel: +44 (0) 1483 683419 or email:
The closing date for applications is Thursday February 28th, 2013.

For further information about the University of Surrey, please visit

We acknowledge, understand and embrace cultural diversity.

2 Postdocs for 3 years in video search: recognition and explanation

*2 POSTDOCS FOR 3 YEARS IN VIDEO SEARCH* Faculty of Science – Informatics Institute

The Informatics Institute at the University of Amsterdam invites applications for 2 Postdocs in Video Search starting Spring 2013.

*/Project Description/*
The positions are part of a 5-year Personal VIDI Grant funded by the Dutch Organization for Scientific Research and headed by Dr. Cees Snoek.
The successful candidates will participate in a frontier research project on video recognition and explanation, and will work in a stimulating environment of a leading and highly active research team including one faculty member and six PhD students. The team has repeatedly won the major visual search competitions, including NIST TRECVID, PASCAL Visual Object Challenge, ImageCLEF, and the ImageNet Large Scale Visual Recognition Challenge.

o PhD in Computer Science, emphasizing computer vision, and/or machine learning.
o Strong publication record in top-tier international conferences and journals.
o Solid knowledge of programming in C/C++ for large-scale processing.
o Motivated and capable to coordinate and supervise PhD research and teaching.

*/Specific to Position nr. 1 (Video recognition)/* o Research record in spatiotemporal descriptors, object localization, action recognition and/or data-intensive machine learning.

*/Specific to Position nr. 2 (Video explanation)/* o Research record in attribute categorization, event recognition, natural language processing, and/or data-intensive machine learning.

Conditions of employment, things we have to offer and application procedure, see:

Call for participation: PASCAL2 IASD challenge

Interactive Annotation of Sequential Data (IASD)
PASCAL2 challenge =================================================================
— Please, accept our apologies in case of multiple receptions —

Dear colleagues,

We are pleased to announce the launch of the Interactive Annotation of Sequential Data (IASD) PASCAL2 challenge. 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:

Feb 12, 2013 – First challenge phase starts.
Mar 12, 2013 – First phase ends: selection of the 3 best systems.
Mar 13, 2013 – Second phase starts (for the 3 best systems only).
Mar 25, 2013 – Second phase ends: winners annouced and ranked.
Apr 2, 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

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

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

Challenge Organizers:

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

Challenge sponsors:

PASCAL2 Network ( EUCOGIII Network ( transLectures project ( Universitat Politecnica de Valencia ( Knowledge for all ( Jožef Stefan Institute (

Call for Papers: IEEE TNNLS Special Issue on “Learning in Non-(geo)metric Spaces”



Special Issue on
Learning in Non-(geo)metric Spaces

Traditional machine learning and pattern recognition techniques are intimately linked to the notion of “feature space.” Adopting this view, each object is described in terms of a vector of numerical attributes and is therefore mapped to a point in a Euclidean
(geometric) vector space so that the distances between the points reflect the observed (dis)similarities between the respective objects.
This kind of representation is attractive because geometric spaces offer powerful analytical as well as computational tools that are simply not available in other representations. However, the geometric approach suffers from a major intrinsic limitation which concerns the representational power of vectorial, feature-based descriptions. In fact, there are numerous application domains where either it is not possible to find satisfactory features or they are inefficient for learning purposes. By departing from vector-space representations one is confronted with the challenging problem of dealing with (dis)similarities that do not necessarily possess the Euclidean behavior or not even obey the requirements of a metric. The lack of “(geo)metric” (i.e., geometric and/or metric) properties undermines the very foundations of traditional machine learning theories and algorithms, and poses totally new theoretical/computational questions and challenges that the research community is currently trying to address. The goal of the special issue is to consolidate research efforts in this area by soliciting and publishing high-quality papers which, together, will present a clear picture of the state of the art.

We will encourage submissions of papers addressing theoretical, algorithmic, and practical issues related to the two fundamental questions that arise when abandoning the realm of vectorial, feature-based representations, namely:

– how can one obtain suitable similarity information from data representations that are more powerful than, or simply different from, the vectorial?
– how can one use similarity information in order to perform learning and classification tasks?

Accordingly, topics of interest include (but are not limited to):

– Embedding and embeddability
– Graph spectra and spectral geometry
– Indefinite and structural kernels
– Game-theoretic models of pattern recognition and learning
– Characterization of non-(geo)metric behavior
– Foundational issues
– Measures of (geo)metric violations
– Learning and combining similarities
– Multiple-instance learning
– Applications

We aim at covering a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, and from theoretical issues to real-world applications.

October 1, 2013 – Deadline for manuscript submission April 1, 2014 – Notification to authors July 1, 2014 – Deadline for submission of revised manuscripts October 1, 2014 – Final decision

Marcello Pelillo, Ca Foscari University, Venice, Italy ( Edwin Hancock, University of York, UK ( Xuelong Li, Chinese Academy of Sciences, China ( Vittorio Murino, Istituto Italiano di Tecnologia & University of Verona, Italy (

1. Read the information for authors at:
2. Submit the manuscript by October 1, 2013 at the TNNLS webpage
( and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript has been submitted to the special issue on “Learning in non-(geo)metric spaces.” Send also an e-mail to M. Pelillo ( with subject “TNNLS special issue submission” to notify the editors of your submission.

New French-Spanish master in Machine Learning and Data Mining

Given their complementary expertise in the fields of Machine Learning and Data Mining, the University of Saint-Etienne (France) and the University of Alicante (Spain) offer a new two-year Master’s program, called MLDM.

MLDM relies on the «Web Intelligence» Master’s carried out by the UJM (Faculté des Sciences et Techniques) and the Ecole Nationale Supérieure des Mines de Saint-Etienne (French side), and the Master’s «Tecnologías de la Informática» of the University of Alicante (Spanish side).

The Master MLDM, which will start on September 2013, is based on the strong scientific collaborations established for many years between the Hubert Curien Laboratory and the Department of Software and Computing Systems, especially in the context of European networks of excellence.

MLDM will provide courses on pattern recognition, machine learning, modeling, knowledge extraction, and data mining and all the basis necessary to understand these topics. These issues have a strong potential for job placement of students in the field of modeling, prediction or decision support, as well as in the area of the Web, image and video processing, health informatics, computer music, robotics, etc.

Courses will be taught in English and are structured according to the European Credit Transfer System with 120 credits over four semesters of full-time studies.

Applicants with at least a BSc degree level (180 ECTS) or equivalent, in computer science, statistics, mathematics or equivalent are invited.

You can find more information on the website: or you can directly send a mail to

The MLDM team.

Conference Call for Papers – INPUTS / OUTPUTS conference – deadline 22 February 2013

Conference Call for Papers
Deadline for contributions 22nd February 2013

INPUTS / OUTPUTS: Interdisciplinary Approaches to Causality in Engagement, Immersion, Presence and Related Concepts in Performance and Human Computer Interaction

26 June 2013
Brighton, UK

Engagement is much sought after in the public discourse of politics, theatre and education. Immersion, presence, and motivation attract further research to the engagement continuum. The goal of this symposium is to inspire an interdisciplinary spectrum of academics, practitioners and funders interested in deeper engagement (and related terms) toward novel collaborative solutions and projects. By mixing practitioners and researchers from arts, media and science, the conference will offer a platform for adaptation of discoveries made in other disciplines.

The title “Inputs/Outputs” concerns the interaction between ‘sender’ and ‘receiver’. Examples of human-centred inputs are computer games, immersive theatre, novels, music, and classroom lessons; examples of outputs are emotions, memories, neural activities, physiological changes, and motivated behaviours.

The rationale for the symposium is to improve the models for understanding the relationship between cause (pre-designed or scripted interventions) and effects (emotions, memories, neural activities) engendered in the audience or end-user. In interactive experiences, proposing causal relationships is made more difficult as human responses are sometimes conflated with causes. The symposium will focus its inter-disciplinary discourse on teasing apart scripted factors (inputs to the audience) that elicit or cause states like engagement, and on the human, observable effects that result from states like engagement (outputs from the audience).

We welcome submissions on the central questions of the conference:
• The relationship between physical, emotional, and intellectual engagement
• Results from assessment and quantification of engagement in different fields
• Methodologies and modalities for measuring engagement in different fields

Other relevant topics include: rapport, immersion, ‘presence’, hypnotic absorption, neuroscience of engagement, interactional synchrony, engagement during interactivity in HCI, social signal processing, games, and the arts.

Presentations should take the form of posters or 15-minute papers. We also welcome proposals for workshops or panels. For posters and individual papers, please submit a 250-word abstract as well as a short biographical note of 100 words. For panel and workshop proposals, please provide a brief outline of the session’s aims together with abstracts and biographical notes for each speaker and for the proposed panel chair or discussants.

Please bear in mind that the conference is an interdisciplinary platform, and that submissions should be easily understood by an audience from outside of your discipline.

All proposals should be emailed in pdf format to the organisers at
All proposals will be acknowledged and successful contributions confirmed no later than 15 March 2013.
For information about speakers and programme, please visit

Postdoctoral Research Associate/Assistant Positions in Machine Learning – UNIVERSITY OF CAMBRIDGE

We are seeking highly creative and motivated postdoctoral Research Associates/Assistants to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK, working with Professor Zoubin Ghahramani. The group is one of the world’s leading centres for Bayesian statistical Machine Learning and successful candidates will be expected to have a strong publication record in this field. Specific areas where we are recruiting include:

– Advanced Bayesian Computation for Cross-Disciplinary Research. The aim of this project is to develop novel advanced algorithms for probabilistic modelling applicable across a range of physical, biological and social sciences.

– Research in areas related to graphical models, statistical time-series modelling, sampling methods, approximate inference, and Bayesian nonparametrics.

– Scalable unsupervised probabilistic modelling for Big Data problems.

The positions are available now and can start as soon as the successful applicant is appointed.

The successful applicant will have or be near completing a PhD in computer science, engineering, statistics or a related area, and will have extensive research experience and a strong publication record in statistics, probability, or machine learning. Preference will be given to applicants with some experience in large-scale modelling with Bayesian methods, non-parametric Bayesian models, and approximate inference.

To apply complete form CHRIS /6 (cover sheet for C.V.s) available at: and send with your C.V. which should include a list of your publications and names of at least two referees, and a covering letter indicating which area you wish to be considered for, in pdf format by email to Diane Unwin, (email , Tel +44 01223 748529).

Applications should be sent so as to reach us by 15th February 2013. Shortlisting will happen soon thereafter.

Quote Reference: NA24722.
Interview Date(s): Interviews will be held with selected candidates as soon as possible after the closing date.

Research post in Probabilistic graphical models in toxicology

Applications are invited for an NC3Rs ( funded postdoctoral position to work jointly with Dr Jon Pitchford
(Mathematics/Biology) and Dr James Cussens (Computer Science) on the research project:

Imprecision and importance: Probabilistic graphical models in toxicology

We plan to use advanced computational and statistical methods to investigate how the evaluation process for the safety of new chemicals can be improved.

We will combine simple stochastic models and Bayesian networks to exploit existing data both to identify the key studies necessary for efficient toxicological assessment, and to quantify what levels of imprecision should be tolerated. The outputs will provide a rigorous quantification of the value of each element of existing protocols.

As well as involving interesting mathematical and computational challenges, we aim for our work to have extensive practical impact:
reduction of the number of animals used for testing where our models indicate that sufficient precision can be derived from a smaller battery of tests; refinement of animal testing protocols where our models identify efficiencies through the holistic assimilation of broad-spectrum data; replacement of animal tests where they can be rationally and quantifiably justified early in a given chemical’s testing strategy.

You will hold the equivalent of a PhD in Mathematics (or similar) by the start date. Applicants with backgrounds in Bayesian statistics, stochastic modelling, and computational algorithms are especially encouraged.

For further details and how to apply go to:

For further information and enquiries, please contact Jon Pitchford
( or James Cussens (

This post is available on a fixed-term basis for up to 33 months and will start on or before 1st July 2013, subject to negotiation.

Interviews will take place on 14 and 15 March 2013.

The University of York is committed to promoting equality and diversity.

Call for applications: Research Fellow in Bioinformatics and Systems Biology (DL Feb 28)

The Department of Information and Computer Science at the Aalto University School of Science is presently looking for a

Research Fellow in Bioinformatics and Systems Biology

The position belongs to the Aalto career system and the selected person will be appointed for a three-year fixed term period. The position allows balanced opportunities for research, teaching and academic leadership through academic coordination of educational programs. The position offers an excellent stepping-stone to future tenure track positions. The Research Fellow will join the research group “Kernel Machines, Pattern Analysis and Computational Biology” led by prof. Juho Rousu, who specializes in network analysis and machine learning for structured data.


We expect the Research Fellow to conduct independent research, and to instruct younger researchers in their work. The Research Fellow will act as the Academic Coordinator of international MSc programmes euSYSBIO (Erasmus Mundus MSc program in Computational and Systems Biology) and MBI (Master’s degree program in Bioinformatics), and teach key courses in the curriculum.


We expect the applicants to have a PhD in Computer Science, Statistics, Computational Biology, Bioinformatics or a related field, with an excellent publication record, and to have completed a successful post-doctoral period. Expertise in one or more research fields mentioned below will be beneficial:

• Algorithm design
• Machine learning and data mining
• Metagenomics
• Biological network analysis
Excellent technical and mathematical skills, as well as excellent written and oral communication skills are required. Good command of English is a necessary prerequisite. A motivated, self-driven working method is appreciated.

Compensation, working hours and place of work

The salary for the position is between 4021 and 4221 EUR per month depending on experience and qualifications. In addition to the salary, the contract includes occupational health benefits, and Finland has a comprehensive social security system. The annual total workload of teaching staff at Aalto University is 1600 hours. The position is located at the Aalto University Otaniemi campus.

Application materials and procedure

The applications are to contain the following documents in one pdf-file in the order given below. The pdf-file is named ‘lastname_firstname_ICSSysBio.pdf’.

• An application letter that includes at least the contact information of the applicant, names and contact information of two senior academics available for reference per e-mail, information whether the application can be used in filling other vacancies at the Department and, from which source the applicant received the information regarding the current call.
• Research plan for the duration of the position,
• A complete curriculum vitae describing education and employment history,
• List of publications, with pointers to openly available online versions of at most three of the most relevant publications.
The application for the position is to be addressed to the Head of the Department of Information and Computer Science at Aalto University, and submitted to the Registry of Aalto University no later than Feb 28, 2013 by email to

Short-listed candidates may be invited for an interview on the Otaniemi campus of Aalto University in Espoo. Should there be a lack of eligible outstanding applicants, the application period may be extended. While all applicants who have submitted an application by the deadline will be appropriately considered, Aalto University reserves the right to consider also other candidates for the announced position.

Additional information

For additional information, please contact Professor Juho Rousu (research related information), tel. +358 50 4151702 or HR Coordinator Stefan Ehrstedt (application process), tel. +358 50 340 7662. Emails:

Espoo, January 30, 2013

About Aalto University

Aalto University is a new university created in 2010 from the merger of the Helsinki University of Technology, Helsinki School of Economics and the University of Art and Design Helsinki. The University’s cornerstones are its strengths in education and research, with 20,000 basic degree and graduate students, and a staff of 5 000 of whom 350 are professors.

About the Department of Information and Computer Science

The research of Information and Computer Science (ICS) department of Aalto department is internationally highly ranked (ARWU “Shanghai” CS 2012: #101-150, NTU “Taiwan”: #83, MAS citations in Machine Learning and Pattern Recognition: #10 in the world). In the recent Research Assessment Exercise covering all the 46 units of Aalto University, ICS department was the top unit of Aalto University, achieving an almost perfect score of 24 out of 25, from review panels assessing the units on a scale of 1 to 5 in the five subareas of scientific quality, scientific impact, societal impact, research environment, and future potential. The Department has a web site at

Call for papers, IDA 2013, October 17-19, 2013, London, England

* * * * * * * * * * * * * * * * * * * * * * * * * * * *


The Twelfth International Symposium on
Intelligent Data Analysis (IDA 2013)

October 17-19, 2013, London, England

* * * * * * * * * * * * * * * * * * * * * * * * * * * *


When the IDA symposium series started in 1995, it focussed on the problem of end-to-end intelligent support for data analysis. In 2010, the IDA symposium re-focussed to support papers that go beyond established technology and offer genuinely novel and “game-changing”
ideas, whilst not always being as fully realised as papers submitted to other conferences.

IDA 2013 continues this approach and seeks “first look” papers that might elsewhere be considered preliminary but contain potentially high impact research. The IDA symposium, which is A-ranked according to ERA, is open to all kinds of modelling and analysis methods, irrespective of discipline. It is expected to be an interdisciplinary meeting that seeks abstractions that cut across domains.


In line with the theme of IDA, the IDA Frontier Prize will be awarded to the most visionary contribution. Submissions considered for this award must present novel and surprising approaches to data analysis. Last years winners can be found at The award consists of a plaque and a prize of 1000 Euros.


IDA solicits papers on all aspects of intelligent data analysis, including papers on intelligent support for modelling and analyzing data from complex, dynamical systems. IDA 2013 particularly encourages papers about:

– Novel applications of IDA techniques to complex systems
– Novel modes of data acquisition and integration
– Robustness and scalability issues of intelligent data analysis techniques
– Visualization and dissemination of results

Intelligent support for data analysis goes beyond the usual algorithmic offerings in the literature. Papers about established technology should offer novel ways to analyzing and/or modelling complex systems to get accepted. The conventional reviewing process, which favours incremental advances on established work, can discourage the kinds of papers that IDA 2013 hopes to publish. The reviewing process will address this issue explicitly: referees will evaluate papers against the stated goals of the symposium, and any paper for which at least one program chair advisor writes an informed, thoughtful, positive review will be accepted irrespective of other reviews.

The proceedings of IDA 2013 will appear in Springer’s Lecture Notes in Computer Science (LNCS) series. For more details on submission and review process, see the IDA webpage at or contact the program chairs. News and updates will be posted on the IDA Twitter account @ida_news.


Deadline for submissions: 6th May, 2013
Author notification: 8th July, 2013
Camera-ready papers due: 5th August, 2013
Conference dates: 17-19 October, 2013


General Chair:
Allan Tucker, Brunel University, UK

Local Chair:
Stephen Swift, Brunel University, UK

Program Chairs:
Frank Hoppner, Ostfalia University of Applied Sciences, Germany Arno Siebes, University of Utrecht, The Netherlands

Poster Chair:
Matthijs van Leeuwen, KU Leuven, Belgium

Frontier Prize Chairs:
Jaakko Hollmen, Aalto University, Finland Frank Klawonn, Ostfalia University of Applied Sciences, Germany

Advisory Chairs:
Xiaohui Liu, Brunel University, UK
Hannu Toivonen, University of Helsinki, Finland