News Archives

Postdoctoral researcher positions in information and computer science (DL 2 April 2012)

The Department of Information and Computer Science (http://ics.aalto.fi/) at Aalto University in Espoo/Helsinki, Finland, pursues research on advanced computational methods for modelling, analysing, and solving complex tasks in technology and science. The research aims at the development of fundamental computer science methods for the analysis of large and high-dimensional data sets, and for the modelling and design of complex software, networking and other computational systems.

To promote its ambitious research agenda, the Department is seeking postdoctoral researchers. While the present call focuses on the topics listed below, outstanding candidates in other areas of information and computer science compatible with the Department’s research mission are also welcome to apply. Applications should be received at latest on 2 April 2012 for full consideration. The Department may decide to make offers to outstanding candidates already before the end of the call.

1. Distributed computing (contact: Prof Keijo Heljanko, keijo.heljanko@aalto.fi)
2. Deep learning (contact: Prof Juha Karhunen, juha.karhunen@aalto.fi)
3. Machine learning in human computer interaction (contact: Prof Samuel Kaski, samuel.kaski@aalto.fi)
4. Statistical data analysis for biomarker discovery and disease prediction (contact: Prof Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)
5. Computational biology and regulatory genomics (contact: Prof Harri Lähdesmäki, harri.lahdesmaki@aalto.fi)
6. Symmetric key cryptanalysis (contact: Prof Kaisa Nyberg, kaisa.nyberg@aalto.fi)
7. Kernel-based learning with multiple outputs, views and models (contact: Prof Juho Rousu, juho.rousu@aalto.fi)
8. Pattern discovery in deep biosphere (contact: Prof Juho Rousu, juho.rousu@aalto.fi)
9. Multi-scale modelling of ecosystems (contact: Prof Juho Rousu and Dr Jaakko Hollmén, juho.rousu@aalto.fi and jaakko.hollmen@aalto.fi)
10. Statistical physics and computer science (contact: Prof Erik Aurell, erik.aurell@aalto.fi)
11. Speech and language processing (contact: Dr Mikko Kurimo, mikko.kurimo@aalto.fi)

Please see the full call text at http://dept.ics.tkk.fi/calls/postdoc_Mar2012/ for application instructions and information about the Department and Aalto University.

CALL FOR PAPERS: The 4th Asian Conference on Machine Learning (ACML2012)

Singapore, November 4-6, 2012

http://acml12.comp.nus.edu.sg/

The 4th Asian Conference on Machine Learning (ACML2012) will be held
on November 4-6, 2012, at the Singapore Management University,
Singapore. The conference aims at providing a leading international
forum for researchers in machine learning and related fields to share
their new ideas and achievements. Submissions from other than the
Asia-Pacific regions are also highly encouraged.

The conference program consist of tutorials, workshops, invited
keynote talks by distinguished researchers as well as single track
sessions of research paper presentations. The invited keynote speakers
for ACML 2012 are James Rehg (Georgia Tech), Dale Schuurmans
(University of Alberta), and Bob Williamson (Australian National
University and NICTA).

The proceedings will be published as a volume of Journal of Machine
Learning Research (JMLR): Workshop and Conference Proceedings. Authors
of selected papers will be invited to submit a significantly extended
version of their paper to a post-conference special issue of the
Machine Learning journal. The BEST STUDENT PAPER will receive an award
sponsored by the Machine Learning journal.

This year ACML will have a new feature: TWO submission deadlines. The
late deadline has the usual “accept” or “reject” outcomes. In addition
to the two outcomes, the early deadline also has “conditional accept
subject to required revisions”, and “resubmit” with notification in
time for them to make the final deadline. The authors of a submission
with a “conditional accept” decision are strongly encouraged to
carefully address the review comments in their revision. The revision
without addressing the review comments properly might be rejected. The
submission of “resubmit” decision must be significantly improved and
revised before it can be re-submitted in the late deadline. Fresh
submissions that have not caught up with the early deadline are also
welcome for the late deadline.

Important dates:
—————-
* Early Paper Submission: 8 May, 2012
* Early Notification: 24 June, 2012
* Final Paper Submission: 24 July, 2012
* Final Notification: 8 Sept, 2012
* Camera ready: 24 Sept, 2012
* Conference: 4-6 November, 2012

ACML2012 calls for papers that report high quality original research
results on machine learning and related fields. The topics include but
are not limited to the following:

1. Learning problems

* Active learning
* Cost-sensitive Learning
* Ensemble methods
* Feature selection/extraction/construction
* Incremental learning and on-line learning
* Learning in graphs and networks
* Multi-agent learning
* Multi-instance learning
* Reinforcement learning
* Semi-supervised learning
* Supervised learning
* Classification, regression, ranking, structured, logical
* Transfer and multi-task learning
* Unsupervised learning
* Clustering, deep learning, latent variable models
* Other learning problems

2. Analysis of learning systems

* Computational learning theory
* Experimental evaluation methodology
* Others

3. Applications

* Bioinformatics
* Collaborative filtering
* Computer vision
* Information retrieval
* Mobile and pervasive computing
* Natural language processing
* Social networks
* Web search
* Other applications

4. Learning in knowledge-intensive systems

* Knowledge refinement and theory revision
* Multi-strategy learning
* Other systems

5. Other learning problems

Abstract and Paper Submission:
——————————
Electronic abstract and paper should be submitted through the ACML
2012 submission site:
https://cmt.research.microsoft.com/ACML2012/

Papers should be written in English and formatted according to the
JMLR Workshop and Conference Proceedings format
(http://jmlr.csail.mit.edu/proceedings/). The maximum length of papers
is 16 pages in this format. Overlength submissions or submissions
without appropriate format will be rejected without review. Paper
submissions should ensure double-blind reviews. Please be sure to
remove any information from your submission that can identify the
authors, including author names, affiliations, self citations and any
acknowledgments.

Proceedings will be published as a volume of JMLR: Workshop and
Conference Proceedings (this is not equivalent to a regular issue of
JMLR) at http://jmlr.csail.mit.edu/proceedings/

Papers submitted to this conference must not have been published,
accepted for publication or be under review for another conference or
journal. Novelty is an important criterion in the selection of
papers.

To encourage interdisciplinary contributions, ACML will welcome papers
that address applications of machine learning in other areas. For
these application papers, the novelty will be judged based on the
applications of machine learning methods. These papers will be
evaluated differently for the algorithmic papers. Authors of these
papers should choose “Applications” as the primary topic.

Submitting a paper to ACML 2012 means that if the paper is accepted,
at least one author will attend the conference to present the
paper. All papers must be submitted electronically in PDF format only,
before the deadline through the submission system. More information,
including detailed author instruction, is available at:
http://acml12.comp.nus.edu.sg/index.php?n=Main.CallForPapers

For questions and suggestions on paper submission, write to:
Steven Hoi < chhoi@ntu.edu.sg > or
Wray Buntine < wray.buntine@nicta.com.au >

Organizers
———-
General Co-Chairs
* Wee Sun Lee (National University of Singapore)
* Zhi-Hua Zhou (Nanjing University)

Program Co-Chairs
* Steven C.H. Hoi (Nanyang Technological University)
* Wray Buntine (NICTA)

Local Arrangement Co-Chairs
* Jing Jiang (Singapore Management University)
* Sintiani Dewi Teddy (Institute for Infocomm Research)
* Ivor Tsang (Nanyang Technological University)

Sponsorship Chair
* David Lo (Singapore Management University)

Finance Chair
* Jianxin Wu (Nanyang Technological University)

Tutorial Co-Chairs
* Hai Leong Chieu (DSO National Laboratories)
* Stanley Kok (Singapore University of Technology and Design)

Workshop Co-Chairs
* David Hardoon (SAS)
* Huan Xu (National University of Singapore)

Publication Chair
* Sinno Jialin Pan (Institute for Infocomm Research)

ACML Steering Committee
* Tom Detterich (Oregon State University, USA)
* Tu Bao Ho (JAIST, Japan)
* Hiroshi Motoda, Chair (Osaka University, Japan)
* Bernhard Pfahringer (Waikato University, New Zealand)
* Masashi Sugiyama (Tokyo Institute of Technology, Japan)
* Takashi Washio (Osaka University, Japan)
* Geoff Webb (Monash University, Australia)
* Qiang Yang (Hong Kong University of Science and Technology, Hong Kong)
* Zhi-Hua Zhou, Co-Chair (Nanjin University, China)

Senior Program Committee
* Edward Chang (Google Research Asia)
* Wei Fan (IBM TJ Watson Lab)
* Xaiofei He (Zhejiang University)
* Tu Bao Ho (JAIST, Japan)
* Chun-Nan Hsu (University of Southern California)
* Aapo Hyvarinen (Helsinki Institute for Information Technology)
* Rong Jin (Michigan State University)
* Kristian Kersting (University of Bonn)
* Irwin King (Chinese University of Hong Kong)
* James Kwok (Hong Kong University of Science and Technology)
* Pavel Laskov (University of Tubingen)
* Yuh-Jye Lee (NTUST)
* YoonKyung Lee (Ohio State University)
* Ping Li (Cornell University)
* Hang Li (Microsoft Research Asia)
* Phil Long (Google)
* Klaus-Robert Muller (Technical University Berlin)
* Bernhard Pfahringer (University of Waikato)
* Scott Sanner (Australian National University)
* Shirish Shevade (Indian Institute of Science)
* Masashi Sugiyama (Tokyo Institute of Technology)
* Jieping Ye (Arizona State University)
* Dell Zhang (Birbeck College University of London)
* Min-Ling Zhang (Southeast University)
* Xiaojin Jerry Zhu (University of Wisconsin (Madison)

JIMSE 2012 Call for Papers: Joint workshop on Intelligent Methods for Software System Engineering

First Call for Papers

JIMSE: Joint workshop on Intelligent Methods for Software System Engineering
****************************************************************************

August 27 or 28, 2012
Montpellier, France

The first Joint workshop on Intelligent Methods for Software System
Engineering will be held in conjunction with the ECAI 2012, the
biennial European Conference on Artificial Intelligence, the leading
conference on Artificial Intelligence in Europe, which will take place
in Montpellier, France, in August, 27-31, 2012.

JIMSE is co-organized by the European Coordination Action EternalS:
Trustworthy Eternal Systems via Evolving Software, Data and Knowledge.

Workshop Description
——————–

The workshop aims at bringing together worldwide stakeholders and
their related communities to discuss current research trends on the
use of intelligent techniques for effective and efficient design of
software systems. To amplify the impact of the workshop, two different
communities sharing the above-mentioned aim will join for the
organization of a large event. These include:

– The Trustworthy Eternal Systems via Evolving Software, Data and
Knowledge (EternalS) community, who has been developing in
conjunction with the homonymous European Coordination Action
(https://www.eternals.eu/). This includes stakeholders of four broad
different ICT areas such as: Learning Systems for Knowledge
Management and Representation, Software Systems, Networked Systems
and Secure Systems. Such community is sharing competencies and
technology for reciprocally improving the specific areas, for
example, applying machine learning for anomaly detection or for
helping establishing network connection between devices.

– The Intelligent Techniques in Software Engineering (ISEW) community,
who has been developing through different workshops (see the past
venues below). The community focuses on intelligent techniques for
addressing, studying, analyzing and understanding critical software
development issues, such as software quality and reliability,
software cost estimation, software requirements, specifications
engineering and software project management.

The above communities focus on traditional AI technologies such as:
(i) fuzzy logic, artificial neural networks, genetic algorithms;
(ii) statistical machine learning (supervised, unsupervised,
semi-supervised learning) and domain adaptation; and (iii) specific
intelligent approaches for text mining & retrieval, graph mining and
ranking algorithms. These are applied to extract patterns and identify
relations regarding (a) the different phases and needs of software
development and analysis as well as (b) designing effective security
policies and networking systems.

Scope and Topics
—————-

We aim at encouraging cross-fertilization of ideas amongst researchers
from different communities. The topics of the workshop regards (but are not
limited to) the application of the following approaches:

* Machine Learning
* Kernel methods
* Text Mining & Retrieval
* Probabilistic Reasoning
* Model Learning
* Expert Systems
* Neural Networks
* Data Mining
* Evolutionary algorithms
* Ranking Algorithms
* Regression models and Statistical methods

TO:

* Software Requirements
* Software Architecture
* Software Methodologies
* Software Algorithms
* Software Design
* Software Performance
* Engineering
* Software Quality & Reliability
* Object-Oriented Analysis and Design
* Software Maintenance & Testing
* Software Metrics
* Software Project Management
* Software Cost Estimation
* Open Source Software
* Software Repository Management
* Cloud computing.

Additionally, we are particularly interested in contributions describing
interdisciplinary researches between the following broad four ICT areas:

(i) Learning Systems for Knowledge/Information Management and Representation.
This area concerns with research for the development of machine learning models,
mainly with applications in the domain of natural language processing, e.g.,

* Information Extraction
* Information Retrieval
* Data Mining
* Semantic Web
* Speech Processing
* Image processing
* Human Computer Interaction

(ii) Software Systems, for example, described by the following keywords:

* Modeling languages
* Feature description languages
* Software product lines
* Feature-oriented programming
* Delta-oriented programming
* Architectural models of diversity
* Formal Methods
* Software evolution
* Component-based systems

(iii) Networked Systems

This area deals with the connection of networked systems over time,
hence addressing eternal interoperability. Related topics include but
are not limited to:

* Connector theory
* Models at runtime
* Protocol learning
* Protocol synthesis
* Runtime verification & validation
* Model-based monitoring
* Interoperable security, privacy & trust

(iv) Secure Systems

This topic area deals with supporting the supervised evolution of
secure systems from the development, deployment, and operation
perspectives. Research in the context of the so-called Future Internet
is particularly welcome. Topics include, but are not limited to:

* Requirements engineering,
* Risk assessment
* Software architectures
* Modeling techniques
* Model-based security techniques
* Secure programming
* Verification and testing
* Software engineering processes for secure and evolvable systems

Important Dates
—————

May 20, 2012: Paper submission deadline
June 28, 2012: Notification of acceptance
July 15, 2012: Camera-ready deadline
July 22, 2012: send PDF to workshop chairs
August 27 or 28, 2012 JIMSE workshop at ECAI 2012

Submission
———-

To promote discussion and the topics of the workshop, we invite the
submission of abstracts of max. 4 pages including references, pictures
and tables, presenting novel research results. The abstracts will be
peer reviewed by the Program Committee (double-blind review
process). Final versions of the extended abstracts (max. 10 pages
including references) will be published in online proceedings, while
selected contributions will appear as post-proceedings in the Springer
CCIS (Communications in Computer and Information Science) series
(pending approval). For further details see
http://www.springer.com/series/7899

Papers should be submitted via the Easychair submission system:

https://www.easychair.org/conferences/?conf=jimse2012

All submissions should be formatted using the ECAI 2012 style file
that can be found at:

http://people.cs.kuleuven.be/~luc.deraedt/ecai2012-style.zip

As the reviewing will be blind, papers must not include the authors’
names and affiliations. Submissions should be in English and should
not have been published previously. If essentially identical papers
are submitted to other conferences or workshops as well, this fact
must be indicated at submission time.

The submission deadline is 23:59 CET on May 20, 2012.

Voice your ideas
—————-

The contributions and the outcome of the discussion that will follow
the paper presentation will be considered for inclusion in the roadmap
that the EternalS coordination action is designing for the European
community: https://www.eternals.eu

The roadmap will be an input to the European Community for the definition
of the Work Programme of 2013.

Tentative Program Committee
—————————

Andreas Andreou, University of Cyprus, Cyprus
Lefteris Angelis, Aristotle University of Thessaloniki, Greece
Roberto Basili, University of Rome Tor Vergara, Italy
Helen Berki, University of Tampere, Finland
Götz Botterweck, Lero, Ireland
Sofia Cassel, University of Uppsala, Sweden
Krishna Chandramouli, Queen Mary University of London, UK
James Clarke, Telecommunications Software and Systems Group, Ireland
Anna Corazza, University of Naples Federico II, Italy
Sergio Di Martino, University of Naples Federico II, Italy
Michael Felderer, University of Innsbruck, Austria
Fausto Giunchiglia, University of Trento, Italy
Reiner Hähnle, TU Darmstadt, Germany
Falk Howar, TU Dordtmund, Germany
Valerie Issarny, INRIA, France
Richard Johansson, University of Gothenburg, Sweden
Jan Jürjens, TU Dortmund, Germany
George Kakarontzas, Technical University of Larisa, Greece
Achilles Kameas, Hellenic Open University, Greece
Basel Katt, University of Innsbruck, Austria
Chris Lokan, UNSW@ADFA, Australia
Ilaria Matteucci, CNR, Italy
Emilia Mendes, University of Auckland, Νew Zealand
Grzegorz Nalepa, AGH University of Science and Technology, Poland
Claudia Niederee, L3S Research Center Hannover, Germany
Animesh Pathak, INRIA, France
Tomas Piatrik, Queen Mary University of London, UK
Hongyang Qu, University of Oxford, UK
Rick Rabiser, JKU Linz, Austria
Vasile Rus, The University of Memphis, USA
Riccardo Scandariato, Katholieke Universiteit Leuven, Belgium
Ina Schaefer, TU Braunschweig, Germany
Holger Schöner, Software Competence Center Hagenberg, Austria
Bernhard Steffen, TU Dortmund, Germany
Christos Tjortjis, The University of Manchester, UK
Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
Daniel Varro, Budapest University of Technology and Economics
Michalis Vazirgiannis, Athens University of Economics & Business
Maria Virvou, University of Piraeus, Greece
Qianni Zhang, Queen Mary University of London, UK

Workshop Chairs
—————

Stamatia Bibi (Aristotle University of Thessaloniki, Greece)
Alessandro Moschitti (University of Trento, Italy)
Barbara Plank (University of Trento, Italy)
Ioannis Stamelos (Aristotle University of Thessaloniki, Greece)

Contact & Website
—————–

For general questions about the workshop, please send an email to
jimse2012@gmail.com

Workshop website: https://sites.google.com/site/jimse2012/

More on Topics and Background
—————————–

During last years Open Source Software has considerably increased,
enabling free and continuing access to publicly available software
engineering data. In turn, this has promoted research on modeling
software development and investigating its various aspects. Though,
software engineering data is available, two important aspects of a
software system have still to be studied such as (i) its
representation in terms of domain knowledge and (ii) the
representation of its design and implementation history. Successful
models for the above points would allow for the design of radically
different paradigms for software development.

Intelligent techniques can be applied for modeling software-related
tasks and providing effective solutions. Machine learning (ML),
knowledge-based systems, and data mining have already been used in
several Software Engineering (SE) tasks. For example, recent
interdisciplinary research in ML and networking systems has shown that
statistically learning can produce large improvement in both
connecting devices (Bennaceur et al., 2011) and modeling their logic
(behavior) (Lamprecht et al., 2011). Moreover, the role ML in software
for security systems is very effective as shown for example in
(Felderer et al., 2011), whereas knowledge-based approaches seem
promising for improving fast prototyping of new product lines as they
can automatize formal verification processes of workflows (Schaefer &
Sauer, 2011).

SE is a conceptual-intensive activity, requiring extensive domain and
software knowledge (Zhang & Zhai, 2005). Software data, such as
requirements, descriptions, change history, design diagrams, size of
programs, tools, packages and methodologies and the source code itself
contain a wealth of information about a project status, progress and
evolution. Intelligent techniques can be used to analyze such data
from past projects to recognize software problems or to learn its
natural evolution during time.

This is very appealing since suggests methods and techniques for
making systems capable of adapting to changes in user requirements and
application domains. Most software systems nowadays are built
iteratively and incrementally, while integrating and interacting with
components from many other systems. Past development models that
presupposed that software systems would not significantly evolve after
delivery are now outmoded. Hence, SE research is studying the design
and implementation of highly evolvable systems. ML is a promising
research direction for the design of adaptable models as they requires
to manage millions of variables in several dimensions, e.g., time,
location, and security conditions, expressing the diversity of the
context in which systems operate. Finally, knowledge and experience
from the development of previous projects can make the use of
supervised learning possible (i.e., training data is available).

References
———-

S. Bibi, G. Tsoumakas, I. Stamelos, I. Vlahavas, Regression via
Classification applied on Software Defect Estimation, Expert Systems
with Applications Journal of Elsevier, Vol 34(3), pp. 2091-2101.

Peter Hearty, Norman E. Fenton, David Marquez, Martin Neil: Predicting
Project Velocity in XP Using a Learning Dynamic Bayesian Network
Model. IEEE Trans. Software Eng. 35(1): 124-137 (2009)

Open source project data: www.sourceforge.org

Adriano L.I. Oliveira, Petronio L. Braga, Ricardo M.F. Lima, Márcio L.
Cornélio, GA-based method for feature selection and parameters
optimization for machine learning regression applied to software
effort estimation, Information and Software Technology, Volume 52,
Issue 11, November 2010, Pages 1155-1166

Stamelos, L. Angelis, P. Dimou, E. Sakellaris, On the use of Bayesian
belief networks for the prediction of software productivity,
Inf. Softw. Technol. 45 (2003) 51–60.

Witten and E. Frank. Data Mining: Practical machine learning tools and
techniques, 2nd Edition. Morgan Kaufmann, San Francisco, 2005.

Eds. Du Zhang , Jeffrey Tsai, “Machine Learning applications in
Software Engineering”, (University of Illinois, Chicago, USA), Series
on Software Engineering and Knowledge Engineering , Vol. 16, 2005.

AmelBennaceur, Richard Johansson, Alessandro Moschitti,
RominaSpalazzese, Daniel Sykes, RachidSaadi, and
ValrieIssarny. Inferring affordances using learning techniques. In The
First Workshop on Trustworthy Eternal Systems via Evolving Software,
Data and Knowledge: EternalS’11. To appear in CCIS Springer, 2011.

Anna-Lena Lamprecht, TizianaMargaria, Ina Schaefer and Bernhard
Steffen, Comparing Structure-oriented and Behaviour-oriented
Variability Modeling for Workflows. In The First Workshop on
Trustworthy Eternal Systems via Evolving Software, Data and Knowledge:
EternalS’11. To appear in CCIS Springer, 2011.

Ina Schaefer and Thomas Sauer, Towards Verification as a Service. In
The First Workshop on Trustworthy Eternal Systems via Evolving
Software, Data and Knowledge: EternalS’11. To appear in CCIS Springer,
2011.

Felderer, M. and Agreiter, B. and Zech, P. and Breu, R. (2011) A
Classification for Model-Based Security Testing. In: The Third
International Conference on Advances in System Testing and Validation
Lifecycle (VALID 2011).

Postdoc positions in Cambridge, UK — image analysis, machine learning, dynamic modelling, computational biology

Dear friends and colleagues,

My lab at the CRUK Cambridge Research Institute has three open postdoc positions for researchers with an image analysis, machine learning, dynamic modelling, or computational biology background.

1. Image analysis and genomics: We will investigate the interplay between genomic features of tumours and the microenvironment in breast cancer, as it is visible in histopathological images.

2. Dynamic modelling: We will combine a dynamic systems biology model of Estrogen Receptor (ER) signaling with a genome-wide map of epigenetic and genetic determinants of ER binding in cell lines and primary breast cancers.

3. Systems genetics: Together with the Ponder lab at CRI we will dissect susceptibility to disease in breast and lung cancer.

More information and links to the official job site, where you need to apply, at http://markowetzlab.org/positions.html

Please forward this email to all suitable candidates.

Kind regards,

Florian


Florian Markowetz

Cancer Research UK
Cambridge Research Institute
Li Ka Shing Centre
Robinson Way, Cambridge, CB2 0RE, UK

phone: +44 (0) 1223 40 4315
fax : +44 (0) 1223 40 4199
email: florian.markowetz@cancer.org.uk
skype: florian.markowetz
blog : http://scientificbsides.wordpress.com
web : http://www.markowetzlab.org

Research Associate in Computational Statistics – UCL

Research Associate in Statistics – Ref:1241425
University College London
Department of Statistical Science

Salary (inclusive of London allowance)
£32,055-£35,707 per annum

To apply: https://atsv7.wcn.co.uk/search_engine/jobs.cgi?owner=5041178&ownertype=fair&jcode=1241425

Deadline: 22 Apr 2012

Applications are invited for a postdoctoral research associate to work with Dr Ricardo Silva on the computational statistics project “Learning Highly Structured Sparse Latent Variable Models”, funded by an EPSRC grant. The project concerns the development of new models and algorithms for measuring unobserved variables in domains such as social sciences and molecular biology. The goal is to generate new smoothing procedures and new methods for detecting structural dependencies induced by unforeseen latent factors.

The post is available from 1 July 2012 (or as soon as possible thereafter) and is funded until 31 July 2013 (or 13 months after the actual starting date) in the first instance.

* Key Requirements
Candidates should have a PhD (or equivalent qualification) or have held a previous postdoctoral position in Bayesian analysis and computational statistics, or very closely related areas. Experience with Markov chain Monte Carlo methods is essential, and knowledge of graphical models, factor analysis and copula modelling desirable.

* Further Details
A job description and person specification can be accessed following the link at the top of this message, together with some details about the department and an application form.

Informal enquiries may be addressed to Dr Ricardo Silva, email: ricardo.silva@ucl.ac.uk, tel: +44(0)20 7679 1879.

For any queries regarding the vacancy or the application process please contact Dr Russell Evans, email: russell.evans@ucl.ac.uk, tel: +44 (0)20 7679 8311.

CFP: BMVC2012 deadline approaching

Just missed the ECCV deadline, then don’t fear, BMVC is almost here

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

The conference will include company exhibits and a demonstration session. All oral presentations will be recorded and hosted on videolectures.net. As with previous years, the best papers of the conference will be invited to submit a journal publication to IJCV.

Conference Chairs: Dr John Collomosse

Dr Krystian Mikolajczyk

Prof Richard Bowden

Invited Speakers: Prof Stan Sclaroff, Boston University, US

Prof Jiri Matas, Czech Technical University, Prague

Tutorials: Large-scale and larger-scale image search, Dr Herve Jegou, INRIA RENNES, France.

MAP inference in Discrete Models, Dr Pushmeet Kohli, Microsoft Research, UK

Important Dates:

26 April 2012 Abstracts due

3 May 2012 Full paper submissions 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

BMVC2012 is sponsored by Pascal2, Microsoft Research and Stemmer Imaging

PAutomaC CFP: Probabilistic Automata learning Competition

=================================================================
PAutomaC Call for Participation
Probabilistic Automata learning Competition
http://ai.cs.umbc.edu/icgi2012/challenge/Pautomac/
=================================================================
— Please, accept our apologies in case of multiple receptions —

PAutomaC is a competition about learning probabilistic finite state models
(PFA, HMM, WA, …). These machines are well-known models for characterizing
the behaviour of systems or processes. A nice feature of these automaton
models is that it is easy to interpret. Unfortunately, in many
applications the original design of a system is unknown. That is why
learning approaches has been used, for instance:
– To modelize DNA or protein sequences in bioinformatics.
– To find patterns underlying different sounds for speech processing.
– To develop morphological or phonological rules for natural language
processing.
– To modelize unknown mechanical processes in Physics
– To discover the exact environment of robots.
– To detect Anomaly for detecting intrusions in computer security.
– To do behavioural modelling of users in applications ranging from web
systems to the automotive sector.
– To discover the structure of music styles for music classification and
generation.
In all such cases, an automaton model is learned from observations of the
system, i.e., a finite set of strings. As the data gathered from
observations is usually unlabelled, the standard method of dealing with
this situation is to assume a probabilistic automaton model, i.e., a
distribution over strings.
This is what the competition is about.

Schedule:

– March: PAutomaC is open, anyone can register, develop and test their
algorithms on training data sets that are available, submit their results
to get feedbacks.
– May 20th: Competition data sets are available
– June 30th: Competition is over
– July 20th: Short papers are submitted
– September 12-15th: special session at ICGI’12 in Washington, DC, USA
(http://www.coral-lab.org/icgi2012/ )

Prizes and publications:

Our sponsor CASL (http://http://www.casl.umd.edu/) will award a prize of
$500 to the winner of the competition.
Participants are encouraged to submit an extended abstract and to present
their innovations at the PAutomaC special session that will be organised
during ICGI 2012.
The winner is expected to submit a paper to the Machine Learning Journal
special issue that will follow the ICGI 2012 conference.

Scientific committee:

– Peter Adriaans, Universiteit van Amsterdam, The Netherlands
– Dana Angluin, Yale University, USA
– Alexander Clark, Royal Holloway University of London, UK
– Pierre Dupont, Université catholique de Louvain, Belgium.
– Ricard Gavalda, Universitat Politècnica de Catalunya, Spain
– Colin de la Higuera, Université de Nantes, France
– Jean-Christophe Janodet, Université d’Evry, France
– Tim Oates, University of Maryland Baltimore County, USA
– Jose Oncina, Universitat de Alicante, Spain
– Menno van Zaanen, Tilburg University, The Netherlands

All informations about the competition can be found on its website:
http://ai.cs.umbc.edu/icgi2012/challenge/Pautomac/
Email contact: pautomac@gmail.com

XRCE Internship: Optimization and Sampling Techniques for Statistical Machine Translation

See: http://www-int.xrce.xerox.com/About-XRCE/Internships/Optimization-and-Sampling-Techniques-for-Statistical-Machine-Translation

Start Date : Around June 2012

Duration : 4-5 months

The MLDAT area (Machine Learning for Document Access and Translation) at XRCE (Xerox Research Centre Europe) is opening an internship to pursue its current research line in Optimization and Sampling Techniques for Statistical Machine Translation.

XRCE has developed some algorithms, which combine optimization and sampling in novel ways in order to perform inference and training for a number of NLP tasks, in the presence of complex feature spaces.

We are looking for a motivated intern to pursue this line of work, further implement these techniques and perform experiments in the context of phrase-based and/or syntax-based translation.

The successful candidate should be enrolled in a graduate program, at the Master or (preferably) PhD level, with focus on Machine Learning, Optimization, Statistical NLP, or (ideally) Statistical Machine Translation.

Strong programming skills (one or several of C/C++, Python, Java…) are a requirement.

IMPORTANT. Priority will be given to students who are members of institutions affiliated to the PASCAL-2 network of excellence. A partial list of such institutions is available at: http://pascallin2.ecs.soton.ac.uk/Network/Sites .

For further details, please contact Marc Dymetman (marc.dymetman@xrce.xerox.com).

Fully funded PhD position in reinforcement learning for information retrieval 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 reinforcement learning for information retrieval. The position is within the Intelligent Systems Lab Amsterdam and will be supervised by dr. Shimon Whiteson and prof. dr. Maarten de Rijke.

Application closing date: 30 April 2012
Starting date: 15 July 2012 (later starting date is possible).
Duration: 4 years.

The research will focus on the development of new algorithms for modeling and learning from implicit feedback, such as click behavior, in order to optimize the behavior of information retrieval systems. Doing so will require new reinforcement learning techniques, as well as other types of machine learning. The new methods will be applied to real data from a leading Dutch social networking site. The project is funded by a grant from the Netherlands Organization for Scientific Research.

Applicants must have a master’s degree in computer science or a closely related area.

In addition, a successful candidate should have:

* strong math skills.

* good knowledge of modern machine learning methods (specific knowledge of reinforcement learning and/or decision-theoretic planning is a plus).

* experience programming in at least one of the following languages: C, C++, Java, Python, or Perl.

* excellent 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 post-doctoral 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 http://tinyurl.com/6od9ulb

Informal inquiries can be made by email to Shimon Whiteson (s.a.whiteson@uva.nl).

Research Associate in Statistics – Ref:1239870

University College London
Department of Statistical Science
www.ucl.ac.uk/stats

Salary (inclusive of London allowance)
£32,055-£36,690 per annum

This is a superb opportunity for an ambitious postdoctoral research associate who will be required to innovate and develop the theory, methods and algorithms defining a range of statistical models, along with associated inferential machinery necessary to represent then analyse the dynamics of the ‘software population’.

Duties and Responsibilities
Applications are invited for a postdoctoral research associate to work with Professor Mark Girolami on the EPSRC Research Programme grant “A Population Approach to Ubicomp System Design”. The primary aim of this project is to deliver a new science of software structures, with theory and tools that reflect software in real world use, and are able to tackle the complex problem of how to design, support change and appropriation. The key concept is the ‘statistical software population’: a statistical model of the variation observed when we look at how the same initial program has been used and adapted by its users. A statistical population model is kept up to date by logging each instance of a program, analysing how it is used and changed both temporally and spatially.

The post is available from April 2012 (or as soon as possible thereafter) and is funded until 1 December 2014 in the first instance with possible extension of a further two years.

Key Requirements
Candidates should have a PhD (or equivalent qualification) and research background in Statistical Methodology or Machine Learning. It is essential that the successful candidate has extensive experience of Computational Statistics, Statistical Modelling, and Bayesian methods in Pattern Analysis.

Further Details
Informal enquiries may be addressed to Professor Mark Girolami, email: m.girolami@ucl.ac.uk, tel: +44(0)20 7679 1861.

Department of Statistical Science
The Computational Statistics research group at UCL offers a vibrant and intellectually stimulating environment for individuals who wish to develop their careers in a world class research environment. UCL is ranked among the top ten research institutions worldwide and has unique strengths in Computational Statistics and Machine Learning. Together with the Gatsby Institute for Computational Neuroscience at UCL, the Departments of Statistical Science and Computer Science form the Centre for Computational Statistics and Machine Learning, (http://www.csml.ucl.ac.uk/) which is part of the European network PASCAL (http://www.pascal-network.org/).

For any queries regarding the vacancy or the application process please contact Dr Russell Evans, email: russell.evans@ucl.ac.uk, tel: +44 (0)20 7679 8311.

https://atsv7.wcn.co.uk/search_engine/jobs.cgi?owner=5041178&ownertype=fair&jcode=1239870
https://www.ucl.ac.uk/statistics/department/jobs/