PhD Studentships in Probabilistic Machine Learning

A number of PhD studentships are available in the newly established

Probabilistic Machine Learning group, headed by Matthias Seeger
School of Computer and Communication Sciences
Ecole Polytechnique Federale de Lausanne (EPFL)
http://upseeger.epfl.ch/

The group focusses on the development and analysis of scalable Bayesian
inference and graphical modelling technology, with challenging applications to
Bayesian experimental design (adaptive compressive sensing, active learning),
medical imaging (magnetic resonance imaging), low-level computer vision, signal
and image processing, and modelling of neural recordings, as well as
learning-theoretical characterizations of such probabilistic setups.

It is part of Europe’s highest ranked computer and communication sciences
faculty at EPFL, one of the leading technical universities worldwide, a unique
surrounding for study and research, where collaborations with top scientists
in computer vision, medical and scientific imaging, signal processing, and
information theory can be forged. EPFL is beautifully located at the shores
of Lake Geneva, offering views of the highest peaks of the Alps, and the
Lausanne area is known for its numerous cultural festivals.

Openings are available for exceptional students with excellent mathematical
background and very high motivation for research in probabilistic machine
learning, approximate Bayesian inference, and applications thereof. Admission
to the doctoral program is internationally competitive.

Application to the doctoral program EDIC is centralized. Please refer to

http://phd.epfl.ch/page-19698-en.html

for any details concerning the application process.

*** Do NOT reply to this mail or send me your documents, but submit them
*** through the site. Applications which are not centrally submitted,
*** cannot be considered.
*** Please indicate in your submission that you would want to work with
*** me (listing other faculty as well is perfectly fine), as this will
*** flag your application for me. You may want to indicate to me that you
*** have applied, but please do not expect a direct answer.

The deadline for applications is

January 15, 2011

Candidates who happen to attend the forthcoming Neural Information Processing
Systems conference (Vancouver, December 2010), should make themselves known
to me there.

Relevant links:

– EDIC doctoral school:
http://phd.epfl.ch/page-19698-en.html
– Research in the Probabilistic Machine Learning group:
http://upseeger.epfl.ch/
– Computer and Communication Sciences, EPFL:
http://ic.epfl.ch/

University of Trento: Post-doc and research professor positions available

The CLIC laboratory of the Center for Mind/Brain Sciences (CIMeC) of
the University of Trento announces the availability of:

TWO 2-YEAR POST-DOC POSITIONS

– one post-doc position in the computational neuroscience of language

– one post-doc position in the construction of multimodal semantic
spaces (partially funded by a Google Research Award)

Read below for details.

The Center for Mind/Brain Sciences (CIMeC) is also offering a number
of attractive

FIXED-TERM RESEARCH PROFESSOR POSITIONS

for advanced post-doctoral researchers with an interest in any area of
the cognitive (neuro)sciences, including language and computational
models.

* Research environment *

The Language Interaction and Computation lab (clic.cimec.unitn.it) is
a unit of the University of Trento’s Center for Mind/Brain Sciences
(www.cimec.unitn.it) or CIMEC: an interdisciplinary center for the
research in brain and cognition including neuroscientists,
psychologists, (computational) linguists, computational
neuroscientists, and physicists. CLIC consists of researchers from the
Departments of Computer Science and Cognitive Science carrying out
research on a range of topics, including concept acquisition and
information extraction from very large multimodal corpora, combining
brain data and data from corpora to study cognition, and methods of
theoretical linguistics.

* Post-doc position in computational neuroscience of language *

A 2-year post-doctoral position in the computational neuroscience of
language with a focus on the organization of conceptual knowledge in
the brain will soon become available at CIMeC/CLIC.

The successful candidate will work as part of a larger project whose
objective is to combine empirical data of different types (corpus
co-occurrence patterns, elicitation experiments, neuroimaging data) to
arrive at a better understanding of the organization of conceptual
knowledge in the mind and brain. Your task will be to continue
on-going work which uses machine learning methods to extract
conceptual representations from recordings of neural activity (EEG,
MEG and fMRI).

The candidate should have technical knowledge of computational
linguistics and machine learning, and familiarity with theories of
ontologies and the lexicon. Programming skills are a must, and
experience with neuroimaging techniques, experimental design
(elicitation/behavioural), machine learning and signal processing
would be a plus.

* Post-doc position in multimodal semantic spaces *

A 2-year post-doctoral position on multimodal semantic spaces is
available at CIMeC/CLIC. The scholarship is partially sponsored by a
Google Research Award, and the project will be carried out as a
collaboration between CLIC members and the Zurich Google Research
team.

The automated measurement of semantic similarity between
words/concepts through semantic space models such as Latent Semantic
Analysis or Topic Models has been a success story in text mining
(Turney and Pantel 2010). Today, through the Web, we have access to
huge amounts of documents that contain both text and images. While the
use of text to improve image labeling and retrieval is an active and
growing area of research (e.g., Wang et al. 2009), in this project we
go the other way around, and develop novel techniques to extract
multimodal semantic spaces from texts and images, in order to improve
the measurement of semantic similarity among words. A recent trend in
computer vision represents images as vectors that record the
occurrence, in the analyzed image, of a discrete vocabulary of “visual
words” (Yang et al. 2007). This development paves the way to the
integration of visual word cooccurrence features into the text-based
vectors of current semantic space models. The topic is expected to
have a strong impact both on the applied front, as a breakthrough in
the acquisition of large semantic repositories, and from a theoretical
point of view, leading to “embodied” models of computational learning
that are more directly comparable to what human learners do (Barsalou,
2008).

The successful candidate will have a strong computational background,
including familiarity with machine learning and/or statistical
methods, and have conducted research in either natural language
processing or computer vision. An interest in exploring the
connections between artificial and natural intelligence and cognition
is also desirable.

* How to apply *

For additional information please send an expression of interest (with
CV) to:

– computational neuroscience: Brian Murphy (brian.murphy(at)unitn.it)
– multimodal semantic spaces: Marco Baroni (marco.baroni(at)unitn.it)
– research professorships: Massimo Poesio (massimo.poesio(at)unitn.it)

Research Fellow and PhD position at Oxford Brookes University

2 year research fellowship start around 1st Feb 2011

And 3.5 year Phd position.

To work with Philip Torr at Oxford Brookes vision research group
http://cms.brookes.ac.uk/staff/PhilipTorr/

contact philiptorr(at)brookes.ac.uk

To engage in state of the art research in computer vision. In particular the work will be an EPSRC grant “Scene Understanding using New Global Energy Models”.

This proposal concerns scene understanding from video. Computer vision algorithms for individual tasks such as object recognition, detection and segmentation has now reached some level of maturity. The next challenge is to integrate all these algorithms and address the problem of scene understanding. The problem of scene understanding involves explaining the whole image by recognizing all the objects of interest within an image and their spatial extent or shape in 3D.

The first application to drive the research will be the problem of automated understanding of cities from video using computer vision, inspired by the availability of massive new data sets such as that of Google’s Street View http://maps.google.com/help/maps/streetview/, Yotta http://www.yotta.tv/index.php (who have agreed to supply Oxford Brookes with data) and Microsoft’s Photosynth http://labs.live.com/photosynth/. The scenario is as follows: a van drives around the roads of the UK, in the van are GPS equipment and multiple calibrated cameras, synchronized to capture and store an image every two metres; giving a massive data set. The task is to recognize objects of interest in the video, from road signs and other street furniture, to particular buildings, to allow them to be located exactly on maps of the environment. A second scenario would be to perform scene understanding for indoor scenes such as home or office, with video taken from a normal camera and Z-cam.

NIPS 2010 Workshop on Modeling Human Communication Dynamics

———————————————————
NIPS Workshop on Modeling Human Communication Dynamics Friday, December 10th,
2010 Whistler, British Columbia, Canada http://projects.ict.usc.edu/hcd2010/
———————————————————

Description

Face-to-face communication is a highly interactive process in which the participants mutually
exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and
the dynamic interactions among an individual’s perceptual, cognitive, and motor processes are
swift and complex. How people accomplish these feats of coordination is a question of great
scientific interest. Models of human communication dynamics also have much potential
practical value, for applications including the understanding of communications problems
such as autism and the creation of socially intelligent robots able to recognize, predict, and
analyze verbal and nonverbal behaviors in real-time interaction with humans.

Modeling human communicative dynamics brings exciting new problems and challenges to
the NIPS community. The first goal of this workshop is to raise awareness in the machine
learning community of these problems, including some applications needs, the special
properties of these input streams, and the modeling challenges. The second goal is to
exchange information about methods, techniques, and algorithms suitable for modeling
human communication dynamics. After the workshop, depending on interest, we may
arrange to publish full-paper versions of selected submissions, possibly as a volume in the
JMLR Workshop and Conference papers series.

Topics

We invite submissions of short high-quality papers describing research on Human
Communication Dynamics and related topics. Suitable themes include, but are not limited to:

* Modeling methods robust to semi-synchronized streams (gestural,
lexical, prosodic, etc.)
* Learning methods robust to the highly variable response lags seen in
human interaction
* Coupled models for the explicit simultaneous modeling of more than
one participant
* Ways to combine symbolic (lexical) and non-symbolic information
* Learning of models that are valuable for both behavior recognition
and behavior synthesis
* Algorithms robust to training data with incomplete or noisy labels
* Feature engineering
* Online learning and adaptation
* Models of moment-by-moment human interaction that can also work for
longer time scales
* Failures and problems observed when applying existing methods
* Insights from experimental or other studies of human communication
* Concrete applications

Invited speakers

* Janet Bavelas (University of Victoria)
* Marian Stewart Bartlett (University of California, San Diego)
* Jeff Bilmes (University of Washington)
* Dan Bohus (Microsoft Research)
* Justine Cassell (Carnegie Mellon University)
* Noah D. Goodman (Stanford University)

Submission guidelines

Submissions should be written as extended abstracts, no longer than
4 pages in the NIPS latex style. NIPS style files and formatting instructions can be found at
http://nips.cc/PaperInformation/StyleFiles
(we will not enforce the double blind rule). Work that was recently published or presented
elsewhere is allowed, provided that the extended abstract mentions this explicitly; work
earlier presented at non-ML venues is especially encouraged. Please send your submission by
email to hcd2010@ict.usc.edu by October 18th, 2010 at 11:59pm PDT.

Important dates

Submission deadline (extended): October 18th, 2010, 11:59pm PDT Notification of
acceptance: November 7th, 2010
Workshop: December 10th, 2010

Organizers

Louis-Philippe Morency, University of Southern California Daniel Gatica-Perez, Idiap
Research Institute Nigel Ward, University of Texas, El Paso

Sponsored by the PASCAL 2 European Network of Excellence on Pattern Analysis,
Statistical Modeling, and Computational Learning

Call for papers – NIPS Workshop – Low-rank Matrix Approximation for Large-scale Learning

===============================================
CALL FOR PAPERS

Low-rank Matrix Approximation for Large-scale Learning
NIPS 2010 Workshop, Whistler, Canada
December 11, 2010

http://www.eecs.berkeley.edu/~ameet/low-rank-nips10/

Submission Deadline: October 31, 2010
===============================================

OVERVIEW

Today’s data-driven society is full of large-scale datasets. In the context of machine learning,
these datasets are often represented by large matrices representing either a set of real-valued
features for each point or pairwise similarities between points. Hence, modern learning
problems in computer vision, natural language processing, computational biology, and other
areas often face the daunting task of storing and operating on matrices with thousands to
millions of entries. An attractive solution to this problem involves working with low-rank
approximations of the original matrix. Low-rank approximation is at the core of widely used
algorithms such as Principle Component Analysis, Multidimensional Scaling, Latent Semantic
Indexing, and manifold learning. Furthermore, low-rank matrices appear in a wide variety of
applications including lossy data compression, collaborative filtering, image processing, text
analysis, matrix completion and metric learning.

The NIPS workshop on “Low-rank Matrix Approximation for Large-scale Learning” aims to
survey recent work on matrix approximation with an emphasis on usefulness for practical
large-scale machine learning problems. We aim to provide a forum for researchers to discuss
several important questions associated with low-rank approximation techniques.
The workshop will begin with an introductory talk and will include invited talks by
Emmanuel Candes (Stanford), Ken Clarkson (IBM
Almaden) and Petros Drineas (RPI). There will also be several contributed paper talks as well
as poster session for contributed papers.

We encourage submissions exploring the impact of low-rank methods for large-scale machine
learning in the form of new algorithms, theoretical advances and/or empirical results. We also
welcome work on related topics that motivate additional interesting scenarios for use of low-
rank approximations for learning tasks. Some specific areas of interest include randomized
low-rank approximation techniques, the effect of data heterogeneity on randomization,
performance of various low-rank methods for large-scale tasks and the tradeoff between
numerical precision and time/space efficiency in the context of machine learning
performance, e.g., classification or clustering accuracy.

SUBMISSION GUIDELINES

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS
latex style. Style files and formatting instructions can be found at
http://nips.cc/PaperInformation/StyleFiles.
Submisssions must be in PDF format. Authors names and affiliations should be included, as
the review process will not be double blind.
The extended abstract may be accompanied by an unlimited appendix and other
supplementary material, with the understanding that anything beyond 4 pages may be
ignored by the program committee.

Please send your PDF submission by email to submit.lowrank@gmail.com by October 31.
Notifications will be given on or before November 15.
Topics that were recently published or presented elsewhere are allowed, provided that the
extended abstract mentions this explicitly.

ORGANIZERS

Michael Mahoney (Stanford), Mehryar Mohri (NYU, Google Research), Ameet Talwalkar
(Berkeley)

PROGRAM COMMITTEE

Alexandre d’Aspremont (Princeton), Christos Boutsidis (Rensselear Polytechnic Institute),
Kamilika Das (NASA Ames Research Center), Maryam Fazel (Washington), Michael I.
Jordan (Berkeley), Sanjiv Kumar (Google Research), James Kwok (Hong Kong University of
Science and Technology), Gunnar Martinsson (Colorado at Boulder)

Reconceiving Machine Learning – Postdoctoral Research Position in Machine Learning at Australian National University

A postdoctoral research position is available at the Australian National University for a
project Reconceiving Machine Learning.

Project: http://users.cecs.anu.edu.au/~williams/rml.html

Job: http://jobs.anu.edu.au/PositionDetail.aspx?p=1541

Closing: 24 October 2010

Postdoctoral Research Position in Machine Learning at Australian National University

There is a 2.5 year postdoctoral research fellow position available immediately at the
Australian National University. The position is associated with the project Structures and
Protocols for inference and the successful application would work with Prof Bob Williamson
and Dr Mark Reid.

For further details and instructions to apply see

http://jobs.anu.edu.au/PositionDetail.aspx?p=1520

Postdoctoral position in Machine Learing, University of Bristol

We are seeking to appoint an outstanding postdoctoral researcher with a background
in machine learning or statistics to contribute to a project with a core aim
of understanding the genetic basis of certain human diseases.

Technological changes in the field of human genetics have resulted in an unprecedented rate of data
generation, offering new opportunities for researchers with expertise in handling, integrating
and analysing large datasets. The goal of this Medical Research Council funded project is to
apply computational and mathematical approaches to the classification and analysis of disease
genes using data from high-throughput genomics platforms, particularly genome-wide
association study data. The post holder will contribute to the development and
application of machine learning and statistical approaches to genetic datasets,
the analysis and interpretation of results and the writing of research papers.

The successful applicant will have knowledge of and the capability to apply advanced
data analysis techniques to data classification and should have a mathematical, computer science or
bioinformatics background. The applicant should be qualified to PhD level (or equivalent) in one or
more of these fields, and should be able to work effectively in a multi-disciplinary
environment.

The successful applicant will have joint membership of the MRC Centre for Causal Analyses
in Translational Epidemiology (CAITE) and the Intelligent Systems Laboratory (ISL) at the
University of Bristol. Dr. Tom Gaunt will act as adviser at CAITE
(see http://www.bristol.ac.uk/caite/) and Dr. Colin Campbell is the adviser
within the ISL (see http://www.enm.bris.ac.uk/research/neural/staff/icgc.html).

Further information and details of the application procedure are available from:

https://www.bris.ac.uk/boris/jobs/feeds/ads?ID=90950

Informal enquiries are welcome, and may be made to Dr. Tom Gaunt
(0117-3310132, tom.gaunt(at)bristol.ac.uk) or Dr. Colin Campbell
(0117-33-15620, C.Campbell(at)bris.ac.uk).

The closing date for applications is: *** 9:00am on 19th November 2010 ***

Grade : Level A in Pathway 2
Starting Salary : £29,853
Timescale of Appointment: fixed term contract for 3 years.

IMAGAPP – FINAL CALL FOR PAPERS – DEADLINE 17th November 2010

IMAGAPP – FINAL CALL FOR PAPERS – DEADLINE 17th November 2010

International Conference on Imaging Theory and Applications

http://www.imagapp.visigrapp.org/home.asp

Algarve, Portugal, 5-7 March 2011

Dear Colleagues,

This is an invitation to submit a paper to the International Conference on Imaging Theory and Applications which is part of VISIGRAPP composed of four co-located conferences, specialized in computer vision, imaging, computer graphics and information visualization (http://www.visigrapp.org/). In additional, this conference is co-located with PECCS 2011 (1st International Conference on Pervasive and Embedded Computing and Communication Systems – http://www.peccs.org/) and registration to VISIGRAPP allows free access to the PECCS conference.

The main topic areas of IMAGAPP are:

· Image Capture, Display and Printing

· Imaging and Video Processing

· Imaging Applications and Services

and paper submissions are due to 17th November 2010 (strict deadline).

More detailed information can be found on the call for papers at http://www.imagapp.visigrapp.org/call_for_papers.asp.

Note that, all accepted papers will be published in the conference proceedings that will be submitted for indexation by several major international indexers, including INSPEC, DBLP, Thomson Reuters Conference Proceedings Citation Index and EI. All presented papers will be also available at the SciTePress Digital Library (http://www.scitepress.org/DigitalLibrary/).

Additionally, a selection of the best papers of the conference will be published by Springer-Verlag in a CCIS Series book. We also intend to publish extended versions of papers in a special issue of The Journal of Convergence Information Technology that are related to a specific theme identified at the conference as most interesting.

Should you have any question please don’t hesitate contacting me or the Imagapp secretariat (imagapp.secretariat (at) insticc.org)

Kind regards,

Gabriela Csurka

IMAGAPP Conference Chair

5 open positions in Brain Computer Interface Company

BitBrain Technologies has 5 open positions in the R&D Department.

BitBrain is a spin-off company from the University of Zaragoza focused
on the development and commercialization of Brain-Computer interfaces
(more info http://www.bitbrain.es)

BitBrain seeks candidates with expertise in:

* Real-time systems, networks, artificial intelligence, C++ programming,
programming with graphical libraries (preferably Qt) and UML.

* Signal processing and biomedical instrumentation, biological signal
analysis and treatment (preferably EEG), biostatistics and pattern
recognition.

Preferred qualifications:

* Expertise in Brain Computer Interfaces or related fields.
* PhD or Masters in Biomedical Engineering, Computer Science or
Electrical Engineering.

BitBrain offers:
* Immediate integration into a young and collaborative team.
* Competitive salaries in a high-tech company.
* Professional carrier with high projection.

BitBrain is located in the CEEI-Aragón of Zaragoza, a 2-minute walk from
the Polytechnic Campus of the University of Zaragoza.

If you are interested, send your CV to info@bitbraintec.com with the
subject: JOB OFFERS 2010.

Best regards,
Cecilio Angulo – UPC, Spain
Javier Minguez – UniZar, Spain