Call for Contributions: NIPS Personalized Medicine Workshop 2011 (NIPS PM 2011)

Call for Contributions:

NIPS 2011 workshop on
“From Statistical Genetics to Predictive Models in Personalized Medicine (NIPS PM 2011)”
Granada, Spain, December 16 or 17, 2011

URL: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/NIPSPM11

Important Dates:

* Deadline for submissions: October 17, 2011
* Notification of acceptance: October 31, 2011

Confirmed Invited Speakers:

* Prof. Dr. Joaquin Dopazo, Head of the Bioinformatics and Genomics Department, CIPF, Valencia, Spain
* Prof. Dr. Bertram Müller-Myhsok, Head of Statistical Genetics Lab, Max Planck Institute for Psychiatry, Munich, Germany

Background:

Technological advances to profile medical patients have led to a change of paradigm in medical prognoses. Medical diagnostics carried out by medical experts is increasingly complemented by large-scale data collection and quantitative genome-scale molecular measurements. Data that are already available as of today or are to enter medical practice in the near future include personal medical records, genotype information, diagnostic tests, proteomics and other emerging ‘omics’ data types.
This rich source of information forms the basis of future medicine and personalized medicine in particular. Predictive methods for personalized medicine allow to integrate these data specific for each patient (genetics, exams, demographics, imaging, lab, genomic etc.), both for improved prognosis and to design an individual-specific optimal therapy.
However, the statistical and computational approaches behind these analyses are faced with a number of major challenges. For example, it is necessary to identify and correcting for structured influences within the data; dealing with missing data and the statistical challenges that come along with carrying out millions of statistical tests. Also, to render these methods useful in practice computational efficiency and scalability to large-scale datasets are an integral requirement. Finally, any computational approach needs to be tightly integrated with medical practice to be actually used and the experiences gained need to be fed back into future development and improvements.
To both address these technical difficulties ahead and to allow for an efficient integration and application in a medical context, it is necessary to bring the communities of statistical method developers, medics and biological investigators together.

Goal:

The purpose of this cross-discipline workshop is to bring together machine learning and healthcare researchers interested in problems and applications of predictive models in the field of personalized medicine. The goal of the workshop will be to bridge the gap between the theory of predictive models and the applications and needs of the healthcare community. There will be exchange of ideas, identification of important and challenging applications and discovery of possible synergies. Ideally this will spur discussion and collaboration between the two disciplines and result in collaborative grant submissions. The emphasis will be on the mathematical and engineering aspects of predictive models and how it relates to practical medical problems.
Although, predictive modeling for healthcare has been explored by biostatisticians for several decades, this workshop focuses on substantially different needs and problems that are better addressed by modern machine learning technologies. For example, how should we organize clinical trials to validate the clinical utility of predictive models for personalized therapy selection? This workshop does not focus on issues of basic science; rather, we focus on predictive models that combine all available patient data (including imaging, pathology, lab, genomics etc.) to impact point of care decision making.
Topics of Interest:
We would like to encourage submissions on any of (but not limited to) the following topics:

* Preventive medicine
* Therapy selection
* Statistical genetics
* Medical genetics
* Precision diagnostics (more precise diagnostics, diseases sub-typing)
* Companion diagnostics/Therapeutics
* Patient risk assessment (for incidence of diseases)
* Personalized medicine
* Integrated diagnostics combining multiple modalities like imaging, genomics and in-vitro diagnostics

Submission Instructions:

We call for paper contributions of up to 8 pages to the workshop using NIPS style. Accepted papers will be presented at the poster session with an additional poster spotlight presentation or full oral presentation. One author of every accepted paper has to be present to present poster and spotlight/talk.
The link to the submission system will be available at http://agbs.kyb.tuebingen.mpg.de/wikis/bg/NIPSPM11

Organizers:

* Karsten Borgwardt, Max Planck Institutes, Germany
* Oliver Stegle, Max Planck Institutes, Germany
* Shipeng Yu, Siemens Healthcare, USA
* Glenn Fung, Siemens Healthcare, USA
* Faisal Farooq, Siemens Healthcare, USA
* Balaji Krishnapuram, Siemens Healthcare, USA

CFP: NIPS 2011 Workshop on Machine Learning and Inference in Neuroimaging

Call for Papers

NIPS 2011 Workshop on Machine Learning and Inference in Neuroimaging

https://sites.google.com/site/mlini2011/

December 16-17, 2011, Melia Sierra Nevada & Melia Sol y Nieve, Sierra
Nevada, Spain

Submission deadline: September 30, 2011

Overview:
————–

Modern multivariate statistical methods have been increasingly applied to various problems in neuroimaging, including “mind reading”, “brain
mapping”, clinical diagnosis and prognosis. Multivariate pattern analysis (MVPA) is a promising machine-learning approach for
discovering complex relationships between high-dimensional signals
(e.g., brain images) and variables of interest (e.g., external stimuli
and/or brain’s cognitive states). Modern multivariate regularization
approaches can overcome the curse of dimensionality and produce highly
predictive models even in high-dimensional, low-sample scenarios
typical in neuroimaging (e.g., 10 to 100 thousands of voxels and just
a few hundreds of samples).

However, despite the rapidly growing number of neuroimaging applications in machine learning, its impact on how theories of brain
function are construed has received little consideration. Accordingly,
machine-learning techniques are frequently met with skepticism in the
domain of cognitive neuroscience. In this workshop, we intend to
investigate the implications that follow from adopting machine-
learning methods for studying brain function. In particular, this
concerns the question how these methods may be used to represent
cognitive states, and what ramifications this has for consequent
theories of cognition. Besides providing a rationale for the use of
machine-learning methods in studying brain function, a further goal of
this workshop is to identify shortcomings of state-of-the-art
approaches and initiate research efforts that increase the impact of
machine learning on cognitive neuroscience.

Moreover, from the machine learning perspective, neuroimaging is a
rich source of challenging problems that can facilitate development of
novel approaches. For example, feature extraction and feature
selection approaches become particularly important in neuroimaging,
since the primary objective is to gain a scientific insight rather
than simply learn a “black-box” predictor. However, unlike some
other applications where the set features might be quite well-explored
and established by now, neuroimaging is a domain where a machine-
learning researcher cannot simply “ask a domain expert what features
should be used”, since this is essentially the question the domain
expert themselves are trying to figure out. While the current
neuroscientific knowledge can guide the definition of specialized
‘brain areas’, more complex patterns of brain activity, such as spatio-
temporal patterns, functional network patterns, and other multivariate
dependencies remain to be discovered mainly via statistical analysis.

The list of open questions of interest to the workshop includes, but
is not limited to the following:
* How can we interpret results of multivariate models in a
neuroscientific context?
* How suitable are MVPA and inference methods for brain mapping?
* How can we assess the specificity and sensitivity?
* What is the role of decoding vs. embedded or separate feature
selection?
* How can we use these approaches for a flexible and useful
representation of neuroimaging data?
* What can we accomplish with generative vs. discriminative modelling?

Workshop Format:
————————–

In this two-day workshop we will explore perspectives and novel
methodology at the interface of Machine Learning, Inference,
Neuroimaging and Neuroscience. We aim to bring researchers from
machine learning and neuroscience community together, in order to
discuss open questions, identify the core points for a number of the
controversial issues, and eventually propose approaches to solving
those issues.

The workshop will be structured around 3 main topics:

– machine learning and pattern recognition methodology
– causal inference in neuroimaging
– linking machine learning, neuroimaging and neuroscience

Each session will be opened by 2-3 invited talks, and an in depth
discussion. This will be followed by original contributions. Original
contributions will also be presented and discussed during a poster
session. The workshop will end with a panel discussion, during which
we will address specific questions, and invited speakers will open
each segment with a brief presentation of their opinion.

This workshop proposal is part of the PASCAL2 Thematic Programme on
Cognitive Inference and Neuroimaging (http://mlin.kyb.tuebingen.mpg.de/).

Paper Submission:
————————–

We seek for submission of original research papers. The length of the
submitted papers should not exceed 4 pages in Springer format (here
are the LaTeX2e style files). We aim at publishing accepted paper
after the workshop in a proceedings volume that contains full papers,
together with review papers by the invited speakers. Authors are
expected to prepare a full 8 page paper for the final camera ready
version after the workshop.

Important dates:
————————–

– September 30, 2011 – paper submission
– October 15th, 2011 – notification of acceptance/rejection
– December 16th – 17th – Workshop in Sierra Nevada, Spain, following
the NIPS conference

Invited Speakers:
————————–

Polina Golland (MIT, US)
James V. Haxby (Dartmouth College, US)
Tom Mitchell (CMU, US)
Daniel Rueckert (Imperial College, UK)
Peter Spirtes (CMU, US)
Gaël Varoquaux (Neurospin/INRIA, France)

Program Committee:
————————–

Guillermo Cecchi (IBM T.J. Watson Research Center)
Melissa Carroll (Google)
Moritz Grosse-Wentrup (Max Planck Institute for Intelligent Systems,
Tübingen, Germany)*
James V. Haxby (Dartmouth College, USA, University of Trento, Italy)
Georg Langs (Medical University of Vienna)*
Bjoern Menze (ETH Zuerich, CSAIL, MIT)
Janaina Mourao-Miranda (University College London, United Kingdom)
Vittorio Murino (University of Verona/Istituto Italiano di Tecnologia,
Italy)
Francisco Pereira (Princeton University)
Irina Rish (IBM T.J. Watson Research Center)*
Mert Sabuncu (Harvard Medical School)
Bertrand Thirion (INRIA, NEUROSPIN)

NIPS 2011 Workshop on Philosophy and Machine Learning — Call for Contributions

Call for Contributions

PhiMaLe 2011

NIPS Workshop on
PHILOSOPHY AND MACHINE LEARNING

Sierra Nevada, Spain
16 or 17 December 2011

http://www.dsi.unive.it/PhiMaLe2011

The fields of machine learning and pattern recognition can arguably be considered as a modern-day incarnation of an endeavor which has challenged mankind since antiquity. In fact, fundamental questions pertaining to categorization, abstraction, generalization, induction, etc., have been on the agenda of mainstream philosophy, under different names and guises, since its inception. Nowadays, with the advent of modern digital computers and the availablity of enormous amount of raw data, these questions have taken a computational flavor.

As it often happens with scientific research, in the early days of machine learning there used to be a genuine interest around philosophical and conceptual issues, but over time the interest shifted almost entirely to technical and algorithmic aspects and became driven mainly by practical applications. In recent years, however, there has been a renewed interest around the foundational and/or philosophical problems of machine learning and pattern recognition, from both the computer scientist’s and the philosopher’s camps. This suggests that the time is ripe to initiating a long-term dialogue between the philosophy and the machine learning communities with a view to foster cross-fertilization of ideas.

In particular, we do feel the present moment is appropriate for reflection, reassessment and eventually some synthesis, with the aim of providing the machine learning field a self-portrait of where it currently stands and where it is going as a whole, and hopefully suggesting new directions. The aim of this workshop is precisely to consolidate research efforts in this area, and to provide an informal discussion forum for researchers and practitioners interested in this important yet diverse subject.

Topics of interest include (but are not limited to):

– Connections to epistemology and philosophy of science
– Essentialism vs anti-essentialism
– Foundations of probability and causality
– Abstraction and generalization
– Connections to decision and game theory
– Similarity and categorization
– The nature of information

The workshop is planned to be a one-day meeting. The program will feature invited as well as contributed oral presentations. We feel that the more informal the better and we would like to solicit open and lively discussions and exchange of ideas from researchers with different backgrounds and perspectives. Plenty of time will be allocated to questions, discussions, and breaks.

Researchers who want to contribute a talk should submit a 4-page extended abstract of their work (using the NIPS style guide) by e-mail to Marcello Pelillo (pelillo@dsi.unive.it) by ***23 October 2011***

The organizers will review all submissions. Notification of acceptance will be sent out by 13 November 2011.

Organizers:

Marcello Pelillo, Ca’ Foscari University, Venice, Italy
Joachim Buhmann, ETH Zurich, Switzerland
Tiberio Caetano, NICTA, Canberra, Australia
Bernhard Schoelkopf, Max Planck Institute for Biological Cybernetics, Germany
Larry Wasserman, Carnegie Mellon University, USA

Internship at Xerox Research Centre Europe: Probabilistic Sampling for Statistical Machine Translation

XRCE (Xerox Research Centre Europe, Grenoble) is opening an internship on “Probabilistic Sampling for Statistical Machine Translation”
http://www.xrce.xerox.com/About-XRCE/Internships/Probabilistic-Sampling-in-Statistical-Machine-Translation

Please contact Marc Dymetman : marc.dymetman(at)xrce.xerox.com if you are interested.

NIPS’11 Workshop on Choice Models and Preference Learning

Submission are invited for the NIPS’11 Workshop on Choice Models and Preference Learning.

Details can be found via: https://sites.google.com/site/cmplnips11/

PhD Studentship in Statistical Machine Learning and Computational Systems Biology (Helsinki, Finland)

PhD studentship in developing novel probabilistic modelling and
statistical inference methodology and applying these methods to
problems in computational systems biology

Department of Information and Computer Science, Aalto University
School of Science (previously Helsinki University of Technology,
http://ics.tkk.fi/en/)

Aalto University School of Science invites applications for a
doctoral student / research assistant
position for a fixed term starting 1 October 2011.

The position is located in the Department of Information and Computer
Science and Helsinki Institute for Information Technology HIIT
Statistical Machine Learning and Bioinformatics research group at the
Aalto School of Science. The focus of the Department’s research and
teaching activity is 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. The department employs approximately 150 people
and operates with a total annual budget of approximately 9 MEUR. The
department hosts two national Centres of Excellence and was ranked
among the top two departments of Aalto University in the Research
Assessment Exercise 2009.

The doctoral student will develop novel probabilistic modelling and
statistical inference methodology and apply these methods to problems
in computational systems biology. The position is related to the
inter-disciplinary European project on Systems approaches to gene
regulation biology through nuclear receptors (SYNERGY), which has been
funded under the ERASysBio+ initiative. The work will take place in
the group of Dr Antti Honkela but it will involve close collaboration
with other project partners, especially Profs. Magnus Rattray and Neil
D. Lawrence (University of Sheffield, UK).

A successful applicant must have a MSc degree in computer science,
electrical engineering, mathematics, physics, or a related field. It
is also possible to start as a research assistant working on one’s
Master’s thesis. A strong mathematical background and an interest in
Bayesian modeling and/or machine learning are necessary. An interest
in computational biology is essential but no prior experience is
necessary.

The application deadline is 13 September 2011.
For more details and application instructions, see
http://www.aalto.fi/en/current/jobs/teaching_and_research/doctoral_student/

NIPS Workshop on Learning Semantics – Call for Abstracts

NIPS 2011 Workshop
Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain.
Saturday December 17, 2011.
http://learningsemanticsnips2011.wordpress.com

OVERVIEW

A key ambition of AI is to render computers able to evolve in and
interact with the real world. This can be made possible only if the
machine is able to produce a correct interpretation of its available
modalities (image, audio, text, …), upon which it would then build a
reasoning to take appropriate actions. Computational linguists use the
term “semantics” to refer to the possible interpretations (concepts)
of natural language expressions, and showed some interest in “learning
semantics”, that is finding (in an automated way) these
interpretations. However, “semantics” are not restricted to natural
language modality, and are also pertinent for speech or vision
modalities. Hence, knowing visual concepts and common relationships
between them would certainly bring a leap forward in scene analysis
and in image parsing akin to the improvement that language phrase
interpretations would bring to data mining, information extraction or
automatic translation, to name a few.

Progress in learning semantics has been slow mainly because this
involves sophisticated models which are hard to train, especially
since they seem to require large quantities of precisely annotated
training data. However, recent advances in learning with weak and
limited supervision lead to the emergence of a new body of research in
semantics based on multi-task/transfer learning, on learning with
semi/ambiguous supervision or even with no supervision at all.
The goal of this workshop is to explore these new directions and,
in particular, to investigate the following questions:

* How should meaning representations be structured to be easily
interpretable by a computer and still express rich and complex knowledge?
* What is a realistic supervision setting for learning semantics? How
can we learn sophisticated representations with limited supervision?
* How can we jointly infer semantics from several modalities?

INVITED SPEAKERS (confirmed)

Chris Burges – Microsoft
Pedro Domingos – University of Washington
Derek Hoiem – UIUC
Raymond Mooney – UT at Austin
Richard Socher – Stanford University
Josh Tenenbaum – MIT

DATES

– Submission deadline: 23:59 EST, Monday, September 26, 2011.
– Acceptance notification: Friday, October 21, 2011.
– Workshop date: Saturday, December 17, 2011.

SUBMISSION

We solicit submission of abstracts to the workshop. Abstracts should
be at most 2 pages long in the NIPS format (excluding references).
Selected abstracts will be presented as posters during a morning and
an afternoon sessions. Submissions should be sent by email to
antoine.bordes(at)hds.utc.fr .

Abstracts should be sent no later than 23:59 EST, Monday, September 26, 2011.

ORGANIZERS

Antoine Bordes – CNRS – UT Compiègne
Jason Weston – Google
Ronan Collobert – IDIAP
Léon Bottou – Microsoft

NIPS Workshop on Relating Machine Learning Problems – An Approach to Unify the Field

Submissions are invited for the NIPS workshop Relating Machine Learning Problems – An Approach to Unify the Field.

Details on the workshop website: http://rml.anu.edu.au

Call for Participation: SIMBAD 2011 — Similarity-Based Pattern Analysis and Recognition (Venice)

CALL FOR PARTICIPATION

SIMBAD 2011

1st International Workshop on Similarity-Based Pattern Analysis and Recognition

28-30 September, 2011
Venice, Italy

http://www.dsi.unive.it/~simbad

(Deadline for Early Registration: 20 September 2011)

MOTIVATIONS AND OBJECTIVES

Traditional pattern recognition techniques are intimately linked to
the notion of “feature spaces.” Adopting this view, each object is
described in terms of a vector of numerical attributes and is
therefore mapped to a point in a Euclidean (geometric) vector space so
that the distances between the points reflect the observed
(dis)similarities between the respective objects. This kind of
representation is attractive because geometric spaces offer powerful
analytical as well as computational tools that are simply not
available in other representations. Indeed, classical pattern
recognition methods are tightly related to geometrical concepts and
numerous powerful tools have been developed during the last few
decades, starting from the maximum likelihood method in the 1920’s, to
perceptrons in the 1960’s, to kernel machines in the 1990’s.

However, the geometric approach suffers from a major intrinsic
limitation, which concerns the representational power of vectorial,
feature-based descriptions. In fact, there are numerous application
domains where either it is not possible to find satisfactory features
or they are inefficient for learning purposes. This modeling
difficulty typically occurs in cases when experts cannot define
features in a straightforward way (e.g., protein descriptors vs.
alignments), when data are high dimensional (e.g., images), when
features consist of both numerical and categorical variables (e.g.,
person data, like weight, sex, eye color, etc.), and in the presence
of missing or inhomogeneous data. But, probably, this situation arises
most commonly when objects are described in terms of structural
properties, such as parts and relations between parts, as is the case
in shape recognition.

In the last few years, interest around purely similarity-based
techniques has grown considerably. For example, within the supervised
learning paradigm (where expert-labeled training data is assumed to be
available) the well-established kernel-based methods shift the focus
from the choice of an appropriate set of features to the choice of a
suitable kernel, which is related to object similarities. However,
this shift of focus is only partial, as the classical interpretation
of the notion of a kernel is that it provides an implicit
transformation of the feature space rather than a purely
similarity-based representation. Similarly, in the unsupervised
domain, there has been an increasing interest around pairwise or even
multiway algorithms, such as spectral and graph-theoretic clustering
methods, which avoid the use of features altogether.

By departing from vector-space representations one is confronted with
the challenging problem of dealing with (dis)similarities that do not
necessarily possess the Euclidean behavior or not even obey the
requirements of a metric. The lack of the Euclidean and/or metric
properties undermines the very foundations of traditional pattern
recognition theories and algorithms, and poses totally new
theoretical/computational questions and challenges.

The workshop will mark the end of the EU FP7 Projects SIMBAD
(http://simbad-fp7.eu), which was devoted precisely to these themes,
and is a follow-up of the ICML 2010 Workshop on “Learning in
non-(geo)metric spaces” (http://www.dsi.unive.it/~icml2010lngs). Its
aim is to consolidate research efforts in this area, and to provide an
informal discussion forum for researchers and practitioners interested
in this important yet diverse subject. We will be covering a wide
range of problems and perspectives, from supervised to unsupervised
learning, from generative to discriminative models, and from
theoretical issues to real-world practical applications.

PROGRAM

The workshop will feature contributed talks and posters as well as
invited presentations by:

– Ulrike Hahn, Cardiff University, UK
– Marco Gori, University of Siena, Italy
– John Shawe-Taylor, University College London, UK

A detailed program can be found at:
http://www.dsi.unive.it/~simbad/index.php/pages/program

We feel that the more informal the better, and we would like to
solicit open and lively discussions and exchange of ideas from
researchers with different backgrounds and perspectives. Plenty of
time will be allocated to questions, discussions, and breaks.

The workshop is supported by PASCAL 2 and IAPR.

ORGANIZATION

Program Chairs
Marcello Pelillo, University of Venice, Italy
Edwin Hancock, University of York, UK

Steering Committee
Joachim Buhmann, ETH Zurich, Switzerland
Robert Duin, Delft University of Technology, The Netherlands
Mario Figueiredo, Technical University of Lisbon, Portugal
Edwin Hancock, University of York, UK
Vittorio Murino, University of Verona, Italy
Marcello Pelillo (chair), University of Venice, Italy

Program Committee
Maria-Florina Balcan, Georgia Institute of Technology, USA
Manuele Bicego, University of Verona, Italy
Joachim Buhmann, ETH Zurich, Switzerland
Horst Bunke, University of Bern, Switzerland
Tiberio Caetano, NICTA, Australia
Umberto Castellani, University of Verona, Italy
Luca Cazzanti, University of Washington, Seattle, USA
Nicolo’ Cesa-Bianchi, University of Milan, Italy
Robert Duin, Delft University of Technology, The Netherlands
Francisco Escolano, University of Alicante, Spain
Mario Figueiredo, Technical University of Lisbon, Portugal
Ana Fred, Technical University of Lisbon, Portugal
Bernard Haasdonk, University of Stuttgart, Germany
Edwin Hancock, University of York, UK
Anil Jain, Michigan State University, USA
Robert Krauthgamer, Weizmann Institute of Science, Israel
Marco Loog, Delft University of Technology, The Netherlands
Vittorio Murino, University of Verona, Italy
Elzbieta Pekalska, University of Manchester, UK
Marcello Pelillo, University of Venice, Italy
Massimiliano Pontil, University College London, UK
Antonio Robles-Kelly, NICTA, Australia
Volker Roth, University of Basel, Switzerland
Amnon Shashua, The Hebrew University of Jerusalem, Israel
Andrea Torsello, University of Venice, Italy
Richard Wilson, University of York, UK

Organization Committee
Samuel Rota Bulo’ (chair), University of Venice, Italy
Nicola Rebagliati, University of Venice, Italy
Furqan Aziz, University of York, UK
Luca Rossi, University of Venice, Italy
Teresa Scantamburlo, University of Venice, Italy

Post-doctoral position in Machine Learning for Biological Networks

Computational Systems Biology and Bioinformatics (CSBB,
http://www.cs.helsinki.fi/group/sysfys/) research group at University of
Helsinki, Department of Computer Science, has an opening for a

POST-DOCTORAL RESEARCHER

in the field of machine learning for biological network inference. The
position is funded by the EU FP7 project “BIO knowLEDGE Extractor and
Modeller for Protein Production” (BIOLEDGE) as well as the National
Centre of Excellence in Algorithmic Data Analysis (ALGODAN), funded by
Academy of Finland.

We expect the applicants to have a PhD (or to have submitted a
dissertation for evaluation) in Computer Science, Statistics,
Computational Biology or related field, with excellent publication
record, as well as experience in one or more of the following fields:
– Kernel methods
– Machine learning for structured data
– Optimisation algorithms
– Large-scale data analysis

In addition, we value experience in the following:
– Biological network reconstruction
– Structured output prediction
– Gene function and interaction prediction

Excellent programming and technical skills, as well as excellent written
and oral communication skills are required.

The successful candidate will join the CSBB research group to develop
new machine learning methods and large-scale optimization algorithms for
biological function and interaction prediction within the BIOLEDGE
project (EU FP7, 2011-2015), a collaboration between Universities of
Helsinki, Cambridge, Malaga as well as VTT Technical Research Centre of
Finland and three SMEs.

Initially a 2-year contract will be offered. Salary is based on demand
level 5 of the salary system of University of Helsinki, corresponding to
monthly gross salary of 3151-3642 euro, depending on individual
performance. An extension of the contract up to 4 years is possible,
depending on performance and availability of funding.

Department of Computer Science, University of Helsinki
(http://www.cs.helsinki.fi/en/home) carries out basic and applied
research in computer science, and offers advanced teaching based on that
research. The department is top-ranked in its field in Finland and has
been elected a national centre of excellence in higher education. The
department offers an excellent environment for high-quality research,
with several world-class research groups in the area of algorithms,
machine learning and data mining. The department has a top-of-the-line
infrastructure, for example a new large-scale computation cluster of ca.
1900 computation cores. University of Helsinki is within the top-20
Universities in Europe. It is continuously rated within top-100 in the
world, e.g., according to http://www.arwu.org/ARWU2010.jsp.
Helsinki is named as number 1 city in Monocle`s annual Quality of Life
survey 2011.

Please send your application to Docent Juho Rousu at
(rekry.bioledge[at]cs.helsinki.fi). Please include CV, transcripts of
your studies, a statement of research interests, as well as names of
possible referees. Applications received by September 23, 2011 will
receive full consideration.

For further information about the position, please contact
Docent Juho Rousu
Department of Computer Science
P.O Box 68
00014 University of Helsinki
tel: +358 50 415 1702
email: firstname.lastname[at]cs.helsinki.fi