Call for Papers CAIP 2013


CAIP 2013

15th International Conference on
Computer Analysis of Images and Patterns

27-29 August 2013

York, United Kingdom


CAIP 2013 is the fiftheenth in the CAIP series of biennial
international conferences devoted to all aspects of Computer
Vision, Image Analysis and Processing, Pattern recognition
and related fields. CAIP2013 will be hosted by York University
and held in August 27-29, in York, UK. The scientific program
of the conference is presented in a single track and the
proceedings of the conference will be published by the
Springer LNCS series.

The scope of CAIP’13 includes, but not limited to, the following
research areas:

– 3D Vision
– 3D TV
– Biometrics
– Color and texture
– Document analysis
– Graph-based Methods
– Image and video indexing and database retrieval
– Image and video processing
– Image-based modeling
– Kernel methods
– Medical imaging
– Mobile multimedia
– Model-based vision approaches
– Motion Analysis
– Natural computation for digital imagery
– Non-photorealistic animation and modeling
– Object recognition
– Performance evaluation
– Segmentation and grouping
– Shape representation and analysis
– Structural pattern recognition
– Tracking
– Applications

Important Dates:

Paper Submission: 1 April 2013
Author Notification: 15 May 2013
Camera-ready paper due: 1 June 2013
CAIP 2013 Conference: 27-29 August 2013

General chair:

Edwin Hancock

Organization Committee:

Adrian G. Bors
Will Smith
Richard Wilson

Workshop ‘Industry & Practices for Forecasting’ in the greater Paris area

This is a call for contributions for the following 3-day workshop:
*Industry & Practices for Forecasting — second edition* to be held 3 miles away from Paris, from June 5 to 7, 2013.

Its focus is on the forecasting of time series using stochastic modeling and/or learning methods, in a high dimensional context.

The purpose is to bring together academics and industry professionals to share different experiences and discuss future trends related to this area.
Both theoretical and practical issues will be considered.
The industries at hand are: energy, finance, transportation, networks, IT, meteorology, health research and environment.

This workshop will consist of plenary sessions (about 1 hour) and contributed sessions (about 30 min).

Plenary talks will be given by:
– Gabor LUGOSI, Pompeu Fabra University, Spain
– Shie MANNOR, Technion University, Israel
– Axel MUNK, Georg-August-University of Gottingen, Germany
– Peter BUHLMANN, ETH Zurich, Switzerland
– Eric KOLACZYCK, Boston University, USA
– Pierre PINSON, DTU, Denmark

For the regular sessions, the program committee chaired by Anestis ANTONIADIS (Université J. Fourier, Grenoble, France) invites the submission of abstracts related the aforementioned topics.

A selection of papers will be invited for inclusion in a proceedings volume (most likely, in some Springer series).

Now that you have most of the information, see
for a more detailed call for submissions.

Gilles Stoltz, on behalf of the program committee of WIPFOR’2013

4 PhD positions and 1 post-doc position in Statistical Machine Translation at the Informatics Institute, University of Amsterdam, The Netherlands.

Apologies for cross-posting.
It would be appreciated if you could forward this to any interested parties.

Applications are invited for four fully-funded 4-year PhD positions and one fully-funded 3-year post-doc position in the area of Statistical Machine Translation. The positions are funded through an advanced research fellowship (Vidi scheme) by the Dutch Science Foundation (NWO) and a governmental research grant.

Please find further details (including the application procedure) for the PhD positions at:

and further details about the post-doc position at:

The application deadline for all positions is 15 December, 2012.
The starting date for all positions is early 2013.

Research Description

The research positions focus on improving state-of-the-art statistical machine translation approaches by investigating how better modeling of the generation process can be utilized to realize more fluent and accurate translations. The research will aim to substantially improve machine translation quality by achieving:

– robust translation quality across different genres, ranging from
formal language use in legal documents to casual language use in
social media;

– improved modeling of domain independent machine translation,
spanning legal, political, entertainment, and sports documents;

– improved modeling of fluency criteria, in particular for languages
for which only limited training data are available.

You will be supervised by Dr. Christof Monz and based in the Informatics Institute at the University of Amsterdam. The Informatics Institute consists of more than 40 members of permanent research faculty, over 25 post-doctoral researchers, and more than 100 PhD students, together representing more than a dozen nationalities.
Members of the institute are actively pursuing a variety of research initiatives, including machine translation, natural language processing, (cross-language) information retrieval, social network analysis, computer vision, machine learning, and multi-agent systems.


You must have an MSc (for the PhD positions) or a PhD (or close to completion, for the post-doc position) in computer science, artificial intelligence, computational linguistics or a closely related area. In addition, you should

– (for the post-doc position only:) have a strong track record of
successful implementation and publication in natural language
processing or machine learning. A background in statistical machine
translation is a plus;

– have strong curiosity to solve problems in natural language processing;

– have a strong background in probability theory, statistics, and
machine learning;

– have excellent programming skills in at least two of the following
languages: C, C++, Java, Python, or Perl;

– enjoy working with real-world problems and real-word, large data

– have excellent communication skills, both oral and written;

– enjoy working in a closely collaborating team.

For specific questions you can get in touch with Christof Monz

Postdoctoral position at the University of Paris-Sud/CNRS

The Machine Learning (AppStat) group of the Linear Accelerator Laboratory (LAL) is seeking a postdoctoral researcher for working on machine learning motivated by experimental physics. The position is financed by the ANR Siminole project ( Some of the ongoing themes are large scale MCMC in hierarchical parametric models, budgeted learning for real-time triggers, and unsupervised (deep) feature learning for next-generation high-resolution pixel calorimeters. All themes include the development of state-of-the-art ML solutions that can make a real difference in both the design and in the data analysis phases of ongoing and future large-scale physics experiments (e.g., Auger, LHCb@CERN, the future ILC or JEM EUSO). The ideal candidate should have a recently completed Ph.D. in the areas of machine learning or computational statistics, and an open spirit to work with researchers of different disciplines.

AppStat ( is an interdisciplinary research group with the mission of creating a scientific link between experimental physics and machine learning. AppStat is part of the Linear Accelerator Laboratory (LAL) and it also has strong ties to the Machine Learning and Optimization team ( of the Computer Science Laboratory (LRI). Both laboratories are part of the University of Paris-Sud campus, located in the outskirts of Paris. The position is available for a period of two years starting in February, 2013. The monthly salary is in the 2500-3000 euro range depending on experience. Interested candidates should send a cover letter, a curriculum vitae, and the names and addresses of three referees before December 20, 2012 to Dr. Balázs Kégl (, and should be ready for an interview in the beginning of January.

PS: I will be at NIPS, don’t hesitate to contact me if you would like to discuss the position.

ESANN 2013: deadline extension

ESANN 2013

21st European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning
Bruges (Belgium) – April 24-25-26, 2013

Submission deadline extension

Due to numerous requests, the deadline to submit papers to the ESANN 2013 conference has been extended to December 7, 2012. Please note that no further extension will be given.

Looking forward to seeing you at ESANN 2013,
The organizing committee.

ESANN – European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning

* For submissions of papers, reviews, registrations:
Michel Verleysen
Univ. Cath. de Louvain – Machine Learning Group
3, pl. du Levant – B-1348 Louvain-la-Neuve – Belgium
tel: +32 10 47 25 51 – fax: + 32 10 47 25 98

* Conference secretariat
d-side conference services
24 av. L. Mommaerts – B-1140 Evere – Belgium
tel: + 32 2 730 06 11 – fax: + 32 2 730 06 00

Positions at LSE

The LSE (London School of Economics) has advertised twenty new positions across all its departments (which include mathematics and statistics) and at all levels, from lecturer (=assistant professor) to professor (=full professor). See
The deadline is 7 December.

LSE, as a Social Sciences university, does not have a computer science department or departments in traditional sciences, but has active research groups in the areas of discrete mathematics and algorithms, game theory, financial mathematics, and many areas of statistics.

It’s an open recruitment exercise, in the sense that departments have not each been given fixed numbers of positions, but essentially will be in competition with each other for the positions. So it is a fairly unusual hiring exercise. When applying, candidates are asked to indicate which department they would want to belong to (and interdisciplinary will be likely to be viewed positively). If you might be interested in applying, with a view to joining the Mathematics department (and/or the Statistics department, with whom we work closely), and would like to discuss, please contact me ( and/or the head of the Mathematics department, Jan van den Heuvel (

Phd position in active perception and control at the University of Amsterdam

The Informatics Institute at the University of Amsterdam invites applications for a fully funded position for a PhD student in the area of active perception and control. The position is within the Intelligent Systems Lab Amsterdam and will be supervised by dr. Gwenn Englebienne and dr. Shimon Whiteson.

Application closing date: 15 December 2012, or until position is filled
Starting date: 1 February 2013
Duration: 4 years

The research will focus on the development of active perception and control algorithms for teams of robots. This will require advancing the state of the art in computer vision, multimodal perception, and decision-theoretic planning and learning. The research will be conducted as part of a European project called “Multi-Robot Cognitive Systems Operating in Hospitals (MOnarCH)” in which the University of Amsterdam collaborates with several other European universities and companies. The project aims to develop a network of heterogeneous robots and sensors for deployment in the pediatric area of an oncological hospital. It will handle uncertainties introduced by people and robots, generate natural interactions, and engage in edutainment activities.

Applicants must have a master’s degree in computer science or a closely related area. In addition, a successful candidate should have:

* strong math and programming skills.

* strong background in artificial intelligence: particularly useful is knowledge of machine learning, reinforcement learning, robotics, and computer vision. Experience with human-computer interaction and multimodal interfaces are a plus.

* strong oral and written communication skills.

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

Some of the things we have to offer:

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

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

For further information, including instructions on submitting an application, see the official job ad at

Informal inquiries can be made by email to Gwenn Englebienne ( and Shimon Whiteson (

PhD studentship in “Machine intelligence” at QMUL

Queen Mary University of London, School of Electronic Engineering and Computer Science
PhD studentship in “Machine intelligence”

Applications are invited for a PhD Studentship starting in September 2013 within the Risk Information Management group.

The focus of this doctoral research project is on lifelong machine learning. Traditional machine learning methods learn each new problem from scratch, requiring extensive training each time. In contrast, humans rapidly learn to solve new and complex problems with limited practice by building on a lifetime of experience with related tasks and domains. The goal of this project is to develop models for lifelong machine learning, enabling experience from each encountered task and domain to be accumulated and exploited in the next. A variety of applications can be considered as lifelong machine learning has potential to impact diverse areas including computer vision, security, forensics, medical diagnosis, big data, ecommerce and others. A strong foundation in mathematics (linear algebra, calculus and statistics) and programming are essential.

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London, in the Risk and Information Management Group which has a world-leading reputation in the area of risk assessment. The RIM group undertakes interdisciplinary research in decision analysis and risk, databases/information retrieval, personalisation, learning, uncertainty, and Bayesian methods. Much of the research involves combining data and human expertise to create intelligent solutions for high stakes decisions. We work with practitioners to produce intelligent ‘unified models’ (typically causal Bayesian networks) that use both data and expertise as inputs, to support expert decision making in multiple application domains. The group is currently working on improved decision making in medical, legal, systems engineering, security and safety applications.
This position, funded by a Queen Mary Prinicipal’s studentship, is for 3 years and will cover student fees and a tax-free stipend starting at £15,590 per annum. Applicants of all nationalities are invited to apply. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, physics or electronic engineering. For queries please contact Dr. Timothy Hospedales

To apply please follow the on-line process (see ) by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page.

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with your name and the student ship title “Machine Intelligence”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at:

The closing date for the applications is 31 January 2013.
Interviews are expected to take place during February 2013.

Postdoc: probabilistic ML and household energy usage

We are seeking a postdoctoral researcher at the University of Edinburgh to work on a project involving probabilistic inference and machine learning methods to help households understand and reduce their energy usage. This position is part of a new £2M project that will involve work on a data set of household energy usage of unprecedented size and scope.

The project would be suitable for candidates with a strong background in probabilistic machine learning who would like to gain more experience in applications of machine learning to sustainability.

The researcher will be a part of the School of Informatics at the University of Edinburgh. This is an opportunity to work in a world-leading machine learning group, including seven faculty in the area. More broadly, a recent international review described the School as an “elite” department of computer science in Europe, and in national research assessment exercises, the School of Informatics has consistently ranked at the top in the UK for research quality.

The postdoctoral researcher will be jointly supervised by Charles Sutton and Nigel Goddard , either of whom may be contacted for informal enquiries.

For more information about the project and information about how to apply, please see

Please note the closing date of FRIDAY 14 DECEMBER 2012, at 5pm UK time.

Research scientist position: statistical genomics and computational biology

We are seeking to employ an enthusiastic Research Scientist/Bioinformatician to join the Statistical Genomics and Systems Genetics Group at the European Bioinformatics Institute (EMBL-EBI) located on the Wellcome Trust Genome Campus near Cambridge in the UK.
The goal of the research group is to devise computational and statistical approaches to understand the interplay of genotype, cellular factors and external influences and their implications for phenotype. Our research is hypothesis-driven and tailored towards answering pertinent biological questions from high-throughput omics datasets.

We combine statistical machine learning with mechanistic modelling concepts to integrate genotype information, molecular profiling data and other phenotypic information. Current research directions include the development of statistical methodology for genome-wide association studies, methods to dissect the genetic architecture of molecular traits and causal modelling to predict functional targets for molecular intervention. Our methodological research aims are embedded in close collaborations with experimental partners.

The successful applicant will hold a doctoral degree or equivalent qualification in computer science, mathematics, physics, and/or engineering. We especially seek candidates with prior experience in statistical aspects of systems biology projects, including gene expression data analysis, GWAS and analysis of NGS data. A foundation and background in statistics, machine learning, optimization and dynamical systems is very beneficial. A background in biology, or previous experience tackling biological questions is desirable but not necessary.
For informal enquiries contact Oliver Stegle . Applications should be made online (deadline 23. November). For further details and application instructions, see