Funded PhD position in machine learning – University of Saint-Etienne (France)

The Machine Learning research group of the University of Saint-Etienne (France) invites applications for a fully funded 3-years PhD position at the Hubert-Curien lab.

Topic of the studentship:
Machine Learning for Image Recognition
(starting date is 1 October 2009)

This project mainly concerns the design of new methods for learning similarities between images that are represented in the form of strings, trees or graphs. The selected candidate will work in the context of the SATTIC (http://labh-curien.univ-st-etienne.fr/wiki-sattic) project, financed by the French National Research Agency. He/She will join the machine learning group composed of about 20 researchers working on the crossroads of machine learning, data mining and information retrieval.

Candidates must have demonstrable interest and expertise in machine learning, statistical theory and have strong programming skills. A background in image processing is encouraged but not required. Applicant should get a Master degree in Computer Science in 2009.

Expressions of interest with a short CV should be sent to: marc.sebban (at) univ-st-etienne.fr by 1 June 2009 at the latest.

Some links:
-Some facts about Saint-Etienne can be found here: http://en.wikipedia.org/wiki/Saint-Étienne.
-Saint Etienne is a medium size city with a quite low cost of living (compared to same size cities in France).
-Paris can be reached from Saint Etienne in less than 3 hours via a direct train. The closest airport is Lyon Saint Exupéry (http://www.lyon.aeroport.fr).
-The city is surrounded by the “regional parc of pilat” in which almost any outdoor activity can be practiced (http://www.parc-naturel-pilat.fr/eng/).
-From a cultural point of view, the art museum in Saint-Etienne holds the second national contemporary art collection, and classic concerts, dance shows, and lyric operas are performed at the Saint-Etienne “Massenet Opera” (http://www.decouvrez-le-votre.com/).

Fourth Summerschool on Advanced Statistics and Data Mining

The Polytechnical Univ. of Madrid organizes a summerschool on “Advanced Statistics and Data Mining” in Madrid between July 6th and July 17th. The summerschool comprises 18 courses divided in 2 weeks.
Attendees may register in each course independently. Registration will be considered upon strict arrival order.

For more information, please, visit
http://www.dia.fi.upm.es/index.php?page=presentation&hl=es_ES or
http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm.

List of courses and brief description
(Full description at http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm)

Week 1 (July 6th – July 10th, 2009)

Course 1: Bayesian networks (15 h), Practical sessions: Hugin, Elvira, Weka, LibB
Bayesian networks basics. Inference in Bayesian networks.
Learning Bayesian networks from data

Course 2: Multivariate data analysis (15 h), Practical sessions: MATLAB
Introduction. Data Examination. Principal component analysis (PCA).
Factor Analysis. Multidimensional Scaling (MDS). Correspondence analysis.
Multivariate Analysis of Variance (MANOVA). Canonical correlation.

Course 3: Dimensionality reduction (15 h), Practical sessions: MATLAB
Introduction. Matrix factorization methods. Clustering methods. Projection methods. Applications

Course 4: Supervised pattern recognition (Classification) (15 h), Practical sessions: Weka
Introduction. Assessing the Performance of Supervised Classification Algorithms.
Classification techniques. Combining Classifiers.
Comparing Supervised Classification Algorithms

Course 5: Introduction to MATLAB (15 h)
Overview of the Matlab suite. Data structures and files. Programming in Matlab.
Visualization tools. Some applications in pattern recognition.

Course 6: Datamining, a practical perspective (15h), Practical sessions: MATLAB, R, Weka
Introduction to Data Mining and Knowledge Discovery. Prediction in data mining.
Classification. Association studies. Data mining in free-form texts: text mining.

Course 7: Time series analysis (15 h), Practical sessions: MATLAB
Introduction. Probability models to time series. Regression and Fourier analysis.
Forecasting and Data mining.

Course 8: Neural networks (15 h), Practical sessions: MATLAB
Introduction to the biological models. Nomenclature. Perceptron networks.
The Hebb rule. Foundations of multivariate optimization. Numerical optimization.
Rule of Widrow-Hoff. Backpropagation algorithm.
Practical data modelling with neural networks

Course 9: Introduction to SPSS (15 h)
Introduction. Describing data. Statistical inference. Time series. Sampling.
Classification and regression

Week 2 (July 13th – July 17th, 2009)

Course 10: Regression (15 h), Practical sessions: SPSS
Introduction. Simple Linear Regression Model. Measures of model adequacy.
Multiple Linear Regression. Regression Diagnostics and model violations.
Polynomial regression. Variable selection. Indicator variables as regressors.
Logistic regression. Nonlinear Regression.

Course 11: Practical Statistical Questions (15 h), Practical sessions: study of cases (without computer)
I would like to know the intuitive definition and use of …: The basics.
How do I collect the data? Experimental design.
Now I have data, how do I extract information? Parameter estimation
Can I see any interesting association between two variables, two populations, …?
How can I know if what I see is “true”? Hypothesis testing
How many samples do I need for my test?: Sample size
Can I deduce a model for my data? Other questions?

Course 12: Missing data and outliers (15 h), Practical sessions: R
Missing Data: Typology of missing data; Simple missing-data methods;
Imputation Methods; Diagnostics and Overimputing. Outliers and robust statistics:
Typology of outliers; Influence measures; Robust methods

Course 13: Hidden Markov Models (15 h), Practical sessions:HTK
Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models.
Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models.
Unit selection and clustering. Speaker and Environment Adaptation for HMMs.
Other applications of HMMs

Course 14: Statistical inference (15 h), Practical sessions: SPSS
Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrapping

Course 15: Features Subset Selection (15 h), Practical sessions: MATLAB, R, Weka
Filter approaches. Wrapper methods. Embedded methods.

Course 16: Introduction to R (15 h)
An introductory R session. Data in R. Importing/Exporting data. Programming in R.
R Graphics. Statistical Functions in R

Course 17: Unsupervised pattern recognition (clustering) (15 h), Practical sessions: MATLAB
Introduction. Prototype-based clustering. Density-based clustering.
Graph-based clustering. Cluster evaluation. Miscellanea

Course 18: Evolutionary computation (15 h), Practical sessions: MATLAB
Genetic algorithms. Genetic programming. Robust and self-adapting intelligent systems.
Introduction to Estimation of Distribution Algorithms.
Improvements, extensions and applications of EDAs. Current research in EDAs.

PhD Studentship in Machine Learning

Applications are invited for a PhD position in the Computational Learning and Computational Linguistics group of the Artificial Intelligence Laboratory, in the Computer Science Department of the University of Geneva, available immediately. The successful candidates will pursue research in connection with a project on machine learning funded by the Swiss National Science Foundation, where they will investigate theoretical and practical issues in statistical modelling of structured objects, such as natural language semantic structures. Further information on related research activities of the CLCL group can be found on the home page of James Henderson.

Candidates should have a solid background in both computer science and mathematics, particularly in statistical learning and/or optimization theory. They should have excellent programming skills as well as communication skills in English (and ideally in French). Preference will be given to candidates with a strong interest and/or experience in computational linguistics or natural language processing. A strong academic record, excellent analytical skills, and a clear aptitude for autonomous, creative research will be priority selection criteria.

The position is available immediately. Starting salary will be around 4400 CHF/month (gross) at the PhD level (1 CHF = 0.66 EUR). Applications will be accepted until the position is filled.

Applicants should send their curriculum vitae, academic transcript, a statement of purpose, and names and addresses (with e-mail and telephone number) of at least 2 references to the address below (preferably by e-mail):

James Henderson
CUI – University of Geneva
Battelle bâtiment A
7 route de Drize
CH-1227 Carouge, Switzerland
E-mail: James.Henderson (at) unige.ch

Informal inquiries should be directed to James Henderson at James.Henderson (at) unige.ch.

Post-doc position at INRIA (LEAR team)

The LEAR team at INRIA Grenoble is looking for a qualified post-doctoral researcher with a specialization in Computer Vision and Machine Learning, on the topic of discovering relationships between actions and objects.

The position is offered at the “Rhone-Alpes” Research Unit of INRIA, located near Grenoble and Lyon. The Unit includes more than 600 people, within 26 research teams and 10 support services.

Starting date: Summer 2009

Deadline for applications: June 2009.

Monthly salary after taxes : 1 983 € (medical insurance included)

Contact: Remi.Ronfard (at) inrialpes.fr

Activities

Recently, a number of image ranking approaches were proposed that build upon visual words similarity networks (i.e. [3,4]). These methods explore relationships between object categories by analyzing similarities of the extracted visual features. In the case of video actions, the relationships are more complex as similarities can be observed in the spaces of image features, motion features, and also in the joint space of image and motion features. An approach to discovering relationships in such networks would allow for recognition of objects, motions, and human-object interactions. The initial investigation can be performed along the lines in [3,4].

In order to achieve the above goal, a good feature extraction method has to be developed. Existing spatio-temporal features describe information of a video subvolume of a simple shape. Intuitively, the procedure that discovers the shapes of such subregions should be guided by some general measure of the subregion descriptiveness. Unfortunately, straightforward extensions of the common 2D subregion extraction methods [1] may not be appropriate. Additionally, approaches to obtaining good descriptors of the extracted subregions should be investigated., with special care taken to obtain good view and time-invariant spatio-temporal descriptors.

In order to investigate the relationships between actions and objects, the problem of analyzing human-object interactions should be addressed. It would be of significant practical benefit to have a method for recognizing interactions from an egocentric

camera. Ideally, the approach would discover atomic interactions from sequences of long-term activities. Some of the possible approaches to implement the idea would be to consider the interaction models [2].

Skills and Profile

* PhD degree (preferably in Computer Vision or Machine Learning)

* Solid programming skills; the project involves programming in Matlab and C++

* Solid mathematics knowledge (especially linear algebra and statistics)

* Creative and highly motivated

* Fluent in English, both written and spoken

* Prior knowledge in the areas of action recognition, video retrieval or object recognition is a plus

REFERENCES

[1] A. Oikonomopoulos, I. Patras, and M. Pantic, Human action recognition with spatiotemporal salient points, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, vol. 36, no. 3, pp. 710-719, 2006.

[2] Hedvig Kjellstrasom, Javier Romero, David Martinez Mercado, and Danica Kragic, Simultaneous visual recognition of manipulation actions and manipulated objects, in ECCV (2), 2008, pp. 336-349.

[3] Gunhee Kim, C. Faloutsos, and M. Hebert,Unsupervised modeling of object categories using link analysis techniques, in CVPR, 2008,pp. 1-8.

[4] Yushi Jing and Shumeet Baluja, Visualrank: Applying pagerank to large-scale image search, TPAMI, vol. 30, no. 11, pp. 1877-1890, 2008.

Research Scientist position – XEROX Research Centre Europe – Grenoble, France

Text and Visual Pattern Analysis

XEROX Research Centre Europe’s Text and Visual Pattern Analysis Area (TVPA) is an expanding team, which specializes in text and visual content understanding. Our mission is the delivery of Xerox’s innovative solutions that make everyday interaction with visual and textual content simple and effective. Our research is the result of combining skills mainly in machine learning, pattern recognition and image analysis. In particular we focus on text and image categorization, image enhancement, quality assessment and document imaging.

Your Job: Research Scientist

As a research scientist in TVPA you will be asked to generate and follow up on new ideas, on build strong competencies and intellectual property in Computer Vision and Pattern Analysis. In particular, you will be pursuing activities around our new research agenda on Applied Visual Aesthetics.

Moreover, you will collaborate in a small, agile team that leads the development of the OMNIA Project. OMNIA is a three year project funded by French Government aiming at developing innovative strategies for multimodal asset retrieval based on three main axes: content, emotion and visual aesthetics.

Research Topics:

* Design of aesthetic measures (light/colour harmony/composition analysis, aesthetic ontology design, user preference regression etc.)

* Image mood analysis (development of features capturing emotional content of visual information, design of classifiers for automatic labeling of assets)

* Assisted content creation and Image personalization (asset selection, features transfer, colour harmonization, etc.)

Responsibilities:

1. Inventing, implementing and evaluating novel imaging software.

2. Studying the state of the art, disseminate results on international conferences and journal papers, fulfill project deliverables.

3. Collaborating with other project partners in order to integrate the research results in a common environment/platform (sharing components, algorithms and methods).

Requirements

– PhD in Computer Science with a strong history of systems building and publishing

-Deep and substantial background on Pattern Recognition/Computer Vision and Image Processing

-Strong English-language written and oral communications skills

Expected start date: Mid June 2009

Type of contract: Temporary position – 18 months

To apply: Please send your CV and cover letter to: luca.marchesotti (at) xrce.xerox.com, xrce-candidates (at) xrce.xerox.com

PhD Position in Machine Translation and Speech Understanding (starting 09/09)

The PORT-MEDIA (ANR CONTINT 2008-2011) is a cooperative project sponsored by the French National Research Agency, between the University of Avignon, the University of Grenoble, the University of Le Mans, CNRS at Nancy and ELRA (European Language Resources Association). PORT-MEDIA will address the multi-domain and multi-lingual robustness and portability of spoken language understanding systems. More specifically, the overall objectives of the project can be summarized as:
– robustness: integration/coupling of the automatic speech recognition component in the spoken language understanding process.
– portability across domains and languages: evaluation of the genericity and adaptability of the approaches implemented in the understanding systems, and development of new techniques inspired by machine translation approaches.
– representation: evaluation of new rich structures for high-level semantic knowledge representation.

The PhD thesis will focus on the multilingual portability of speech understanding systems. For example, the candidate will investigate techniques to fast adapt an understanding system from one language to another and creating low-cost resources with (semi) automatic methods, for instance by using automatic alignment techniques and lightly supervised translations. The main contribution will be to fill the gap between the techniques currently used in the statistical machine
translation and spoken language understanding fields.

The thesis will be co-supervised by Fabrice Lefèvre, Assistant Professor at LIA (University of Avignon) and Laurent Besacier, Assistant Professor at LIG (University of Grenoble). The candidate will spend 18 months at LIG then 18 months at LIA.

The salary of a PhD position is roughly 1,300€ net per month. Applicants should hold a strong university degree entitling them to start a doctorate (Masters/diploma or equivalent) in a relevant discipline (Computer Science, Human Language Technology, Machine Learning, etc). The applicants should be fluent in English. Competence in French is optional, though applicants will be encouraged to acquire this skill during training. All applicants should have very good programming skills.

For further information, please contact Fabrice Lefèvre (Fabrice.Lefevre at univ-avignon.fr) AND Laurent Besacier (Laurent.Besacier at imag.fr).

MCBR-CDS09: CALL FOR PAPERS

CALL FOR PAPERS
MCBR-CDS 2009: Medical Content-based Retrieval for Clinical Decision Support
September 20th, 2009
London, UK
http://www.almaden.ibm.com/cs/projects/aalim/multimodal-decision.html

** Paper Submisions Due May 22th, 2008 **

——————-
Call for Papers
——————-

Diagnostic decision making (using images and other clinical data) is still very much an art for many physicians in their practices today due to a lack of quantitative tools and measurements. Traditionally, decision making has involved using evidence provided by the patient’s data coupled with a physician’s a priori experience of a limited number of similar cases. With advances in electronic patient record systems, a large number of pre-diagnosed patient data sets are now becoming available. These datasets are often multimodal consisting of images (x-ray, CT, MRI), videos and other time series, and textual data (free text reports and structured clinical data). Analyzing these multimodal sources for disease-specific information across patients can reveal important similarities between patients and hence their underlying diseases and potential treatments. Researchers are now beginning to use techniques of content-based retrieval to search for disease-specific information in modalities to find supporting evidence for a disease or to automatically learn associations of symptoms and diseases. Benchmarking frameworks such as ImageCLEF (Image retrieval track in the Cross-Language Evaluation Forum) have expanded over the past five years to include large medical image collections for testing various algorithms for medical image retrieval. This has made comparisons of several techniques for visual, textual, and mixed medical information retrieval as well as for visual classification of medical data possible based on the same data and tasks.
The goal of this workshop is to bring together researchers in medical imaging, medical image retrieval, data mining, text retrieval, and machine learning/AI communities to discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. We are looking for original, high-quality submissions that address innovative research and development in the analysis, search and retrieval of multimodal medical data for use in clinical decision support. Further, to encourage a larger group of image analysis researchers to profit from the databases and evaluations created in the context of ImageCLEF, we will provide access to the medical databases and tasks of ImageCLEF 2009 which has obtained rights from RSNA to use over 70,000 images of the journals Radiology and Radiographics.

Topics for the workshop include but are not limited to:
–Mining of multimodal medical data (X-ray, MRI, CT, echo videos, time series data)
–Machine learning of disease correlations from mining multimodal data
–Algorithms for indexing and retrieval of data from multimodal medical databases
–Disease model-building and clinical decision support systems based on multimodal analysis
–Practical applications of clinical decision support using multimodal data retrieval or analysis
–Algorithms for medical image retrieval

————————–
Paper Submission
————————–
Prospective authors are invited to submit papers of not more than
eight(8) pages including results, figures and references. Please use
the MICCAI author kit to format the papers.

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Important Dates
————————
Paper submission deadline: May 22nd, 2009

Notification of acceptance: June 28th, 2009

Camera ready copy : July 20th, 2009

Workshop date: September 20th 2009

Extended CfP: NUMML 2009 (ICML 2009 Workshop on Numerical Mathematics in Machine, Learning)

Extended: CALL FOR CONTRIBUTIONS

International Conference on Machine Learning (ICML)

Workshop on Numerical Mathematics in Machine Learning

June 18, 2009. Montreal, Canada

http://numml.kyb.tuebingen.mpg.de

*Deadline for abstract submissions: 8th May, 2009 (EXTENDED Date)*

Most machine learning (ML) algorithms rely fundamentally on concepts of numerical mathematics. Standard reductions to black-box computational primitives do not usually meet real-world demands and have to be modified at all levels. The increasing complexity of ML problems requires layered approaches, where algorithms are components rather than stand-alone tools fitted individually with much human effort. In this modern context, predictable run-time and numerical stability behavior of algorithms become fundamental. Unfortunately, these aspects are widely ignored today by ML researchers, which limits the applicability of ML algorithms to complex problems.

Our workshop aims to address these shortcomings, by trying to distill a compromise between inadequate black-box reductions and highly involved complete numerical analyses. We will invite speakers with interest in *both* numerical methodology *and* real problems in applications close to machine learning. While numerical software packages of ML interest will be pointed out,
our focus will rather be on how to best bridge the gaps between ML requirements
and these computational libraries. A subordinate goal will be to address the role of parallel numerical computation in ML.

Examples of machine learning founded on numerical methods include low level computer vision and image processing, non-Gaussian approximate inference, Gaussian filtering / smoothing, state space models, approximations to kernel methods, and many more.

The workshop will comprise a panel discussion, in which the invited speakers are urged to address the problems stated above, and offer individual views and suggestions for improvement. We highly recommend active or passive attendance at this event. Potential participants are encouraged to contact the organizers beforehand, concerning points they feel should be addressed in this event.

Invited Speakers:

Inderjit Dhillon University of Texas, Austin
Michael Mahoney Stanford University
Jacek Gondzio Edinburgh University, UK
Dmitry Malioutov MIT

Topics:

Potential short talks / posters should aim to address:

– Raising awareness about the increasing importance of stability and predictable run-time behaviour of numerical machine learning algorithms and primitives
– Stability and predictable behaviour as a criterion for making algorithm choices in machine learning
– Lessons learned (and not learned) in machine learning about numerical mathematics. Ideas for improvement
– Novel developments in numerical mathematics, with potential impact on machine learning problems

Contributions will be considered only if a clear effort is made to analyze problems that arise, and if choices of algorithms, preconditioning, etc. are clearly motivated. For reasons stated in “Motivation”, submissions that apply numerical methods in a black box fashion, or that treat numerical techniques without motivating the use for machine learning, cannot be considered. The usual “smoothing over problems” conference paper style is discouraged, and naming and analyzing problems is strongly encouraged.

Potential Subtopics (submissions are not limited to these):

A- Solving large linear systems
-Arise in the linear model/Gaussian MRF (mean computations), nonlinear optimization methods (Newton-Raphson, IRLS, …)
-Preconditioning, use of model structure.
-Our main interest is on semi-generic ideas that can be applied to a range of machine learning real-world situations
B- Novel numerical software developments relevant to ML
-Parallel implementations of LAPACK, BLAS
-Sparse matrix packages
C- Approximate eigensolvers
-Arise in the linear model (covariance estimation), spectral clustering and graph Laplacian methods, PCA
-Lanczos algorithm and specialized variants
-Preconditioning
D- Exploiting matrix/model structure, fast matrix-vector multiplication
-Matrix decompositions/approximations
-Multi-pole methods
-Signal-processing primitives (e.g., variants of FFT)
F- Parallel numerical computation for ML
G- Other numerical mathematics (ODEs, PDEs, Quadrature, etc.) focusing on machine learning

Submission Instructions:

We invite submissions of extended abstracts, from 2 to 4 pages in length (using the ICML 2009 style file). Criteria for content are given in “Topics”. Submissions should be sent to suvadmin (at) googlemail.com

Accepted contributions will be allocated short talks or posters. There will be a poster session with ample time for discussion. Short talk contributions are encouraged to put up posters as well, to better address specific questions.

Important Dates:

Submissions due: May 8, 2009
Author notification: May 18 , 2009
Workshop date: June 18, 2009

Organizers:

Matthias W. Seeger MPI Informatics / Saarland University, Saarbruecken
Suvrit Sra MPI Biological Cybernetics, Tuebingen
John P. Cunningham Stanford University (EE), Palo Alto

We acknowledge financial support through the PASCAL 2 Initiative of the European Union.

SMART workshop Call for Participation

CALL FOR PARTICIPATION

Statistical Multilingual Analysis for Retrieval and Translation – Barcelona 2009
http://patterns.enm.bris.ac.uk/smart-dissemination-workshop

Barcelona May 13, 2009
Venue: Aula Teleensenyament (Tele-teaching room) in building B3 of the Campus Nord of the UPC

A joint event of SMART project – PASCAL Network jointly-located with EAMT-2009

Co-organizers: Marco Turchi, Nello Cristianini, Xavier Carreras, Tijl de Bie

The aim of this workshop is to disseminate scientific results produced by the SMART project to the larger technical and scientific community working on Machine Translation. To facilitate this inter-exchange, it will be co-located with the EAMT 2009 – 13th Annual Conference of the European Association for Machine Translation that will be held May 14-15, 2009 Universitat Politècnica de Catalunya, Barcelona, Spain.

Conference web site: http://www.talp.cat/eamt09

Workshops page: http://www.talp.cat/eamt09/index.php/associated-workshops

Programme

Morning

9.30 – 10.00 Welcome, Nicola Cancedda, Xerox Research Centre Europe

10.00 – 11.00 Invited Talk: “Empirical Machine Translation and its Evaluation” – Jesus Gimenez, UPC

11.00 – 11.30 Coffee

11.30 – 12.00 – “Online learning for CAT applications” – Nicolo` Cesa-Bianchi, University of Milan

12.00 – 12.30 – “Sinuhe — Statistical Machine Translation with a Globally Trained Conditional Exponential Family Translation Model” – Matti T Kaariainen, University of Helsinki

12.30 – 1300 – “Large scale, maximum margin regression based, structural learning approach to phrase translations” – Sandor Szedmak, University of Southampton

LUNCH

Afternoon

14.00 – 14.30 “Learning to Translate: statistical and computational analysis” – Marco Turchi, University of Bristol

14.30 – 15.00 -“Detecting and exploiting Translation Direction” – Cyril Goutte, National Research Council – Canada

15.00 – 15.30 – “Multi-view CCA and regression CCA” – Blaz Fortuna, Jo¾ef Stefan Institute

Coffee

16.00 – 16.30 – “Large-Margin Structured Prediction via Linear Programming” – Zhuoran Wang, University College London

16.30 – 17.00 – “Confidence Estimation for Machine Translation” – Lucia Specia, Xerox Research Centre Europe

17.00 Closing Remarks

ABOUT THIS WORKSHOP

A joint event of SMART project – PASCAL Network

SMART (Statistical Multilingual Analysis for Retrieval and Translation, www.smart-project.eu) is a 3-year “Specific Target Research Project” (STReP) funded by the European Commission. SMART is
an attempt to address different problems of Machine Translation and Cross-Language Information Retrieval by the methods of modern Statistical Learning.

In the first two years of the project, the scientific focus has been on developing new and more effective statistical approaches while ensuring that existing know-how is duly taken into account. This was done by bringing together leading research institutions in Statistical Learning, Machine Translation and Textual Information Access.

PASCAL 2 (Pattern Analysis, Statistical Modelling and Computational Learning 2) is a 5-year “Network of Excellence” (NoE) funded by the European Commission, focusing on Machine Learning, Statistics and Optimization.

The aim of this workshop is to disseminate scientific results and share experiences produced by the SMART project to the larger technical and scientific community. The SMART consortium considers
this workshop to be a great opportunity for science investigations, creating both scientific and commercial opportunities as well as technological challenges to researchers.

Funded PhD post in data mining / machine learning – University of Bristol

Applications are invited for a fully funded PhD studentship (fees and stipend) in the Pattern Analysis and Intelligent Systems research group at the University of Bristol, UK.

Topic of the studentship:
“Statistical techniques for informative pattern mining in complex and structured data”
However, applicants interested in
“Machine Learning and Data Mining for Music Information Retrieval”
are also welcome to apply.

You will join a vibrant team working on the crossroads of data mining, machine learning, complexity science, and on applications in areas including bioinformatics, music information retrieval, web mining, news media analysis, and social network analysis.

The duration of the studentship is 3.5 years, and the starting date is 1 October 2009 or shortly after that (to be agreed).

The ideal candidate has a first class computer science / electrical engineering / mathematics / physics degree, with a strong background in mathematics as well as programming experience. (S)he is a loyal team player, and combines an interest in data mining and machine learning theory with a commitment to applying theoretical results in a real context, with a strong desire to make an impact.

Expressions of interest with a short CV, or any informal queries, should be sent to:
tijl.debie (at) bristol.ac.uk

Some links:
The Pattern Analysis and Intelligent Systems research group: http://patterns.enm.bris.ac.uk/,
part of the Intelligent Systems Lab: http://intelligentsystems.bristol.ac.uk/,
part of the University of Bristol: http://www.bristol.ac.uk/
part of the very enjoyable city of Bristol: http://en.wikipedia.org/wiki/Bristol