CFC: NIPS workshop on “Cosmology Meets Machine Learning”

Call for Contributions

NIPS 2011 Workshop on
“Cosmology Meets Machine Learning”
Sierra Nevada, Spain, December 16 or 17, 2011.

URL: http://webdav.is.mpg.de/pixel/cmml-nips2011.html

Submission for contributions is now open.
For more information please visit the meeting webpage.

Join us for an exciting program including invited talks by:

* Prof. Dr. Anthony Tyson, UC Davis
* Prof. Dr. Alexandre Refregier, ETH Zurich
* Prof. Dr. Jean-Luc Starck, CEA Saclay Paris
* Prof. Dr. David Hogg, New York University

Important Dates
—————

* November 2, 2011 Abstract submission deadline
* November 12, 2011 Notification of acceptance
* December 16 or 17, 2011 Workshop

Description
———–

Cosmology aims at the understanding of the universe and its evolution
through scientific observation and experiment and hence addresses one
of the most profound questions of human mankind. With the
establishment of robotic telescopes and wide sky surveys cosmology
already now faces the challenge of evaluating vast amount of data.

Multiple projects will image large fractions of the sky in the next
decade, for example the Dark Energy Survey will culminate in a
catalogue of 300 million objects extracted from peta-bytes of
observational data. The importance of automatic data evaluation and
analysis tools for the success of these surveys is undisputed.

Many problems in modern cosmological data analysis are tightly related
to fundamental problems in machine learning, such as classifying stars
and galaxies and cluster finding of dense galaxy populations. Other
typical problems include data reduction, probability density
estimation, how to deal with missing data and how to combine data from
different surveys.

An increasing part of modern cosmology aims at the development of new
statistical data analysis tools and the study of their behaviour and
systematics often not aware of recent developments in machine learning
and computational statistics.

Therefore, the objectives of this workshop are two-fold:

(i) The workshop aims to bring together experts from the Machine
Learning and Computational Statistics community with experts in the
field of cosmology to promote, discuss and explore the use of machine
learning techniques in data analysis problems in cosmology and to
advance the state of the art.

(ii) By presenting current approaches, their possible limitations, and
open data analysis problems in cosmology, this workshop aims to
encourage scientific exchange and to foster collaborations among the
workshop participants.

Submission Instructions
———————–

We invite submission of abstracts on topics in the following areas:

* challenging problems in cosmology data analysis
* applications of machine learning methods in cosmological data analysis problems

Submissions should not exceed 200 words and will be judged on
technical merit, the potential to generate discussion, and their
ability to foster collaboration within the workshop participants.
Accepted papers will be presented at the poster session with an
additional poster spotlight presentation. One author of every accepted
paper has to attend the workshop to present poster and spotlight talk.

Submissions should be sent to cmml.nips2011(at)gmail.com

Organizing Committee
——————–

Michael Hirsch, Universtiy College London
Sarah Bridle, University College London
Stefan Harmeling, Max Planck Institute for Intelligent Systems
Phil Marshall, Oxford University
Mark Girolami, University College London
Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems

DEADLINE EXTENSION – EACL 2012: call for tutorial proposals

** Deadline extension: October 13th, 2011

Proposals are invited for the Tutorial Program of the 13th Conference
of the European Chapter of the Association for Computational
Linguistics (EACL 2012), to be held in Avignon, France, from April 23
to April 27, 2012. The selected tutorials will be given on the Monday
and Tuesday preceding the main conference (April 23 and 24).

EACL 2012 seeks proposals for tutorials in all areas of computational
linguistics, broadly conceived to include disciplines such as
linguistics (including phycholinguistics and other subfields), speech,
information retrieval and multimodal processing.

We particularly welcome (1) tutorials which cover advances in newly
emerging areas not previously covered in an (E)ACL related tutorial,
or (2) tutorials which provide introductions into related fields which
are potentially relevant for the CL community (e.g. bioinformatics,
social media, human language processing, machine learning
techniques). In order to gather a widespread audience, the interest of
the tutorial and the quality of the instructors will also be taken
into account.

REMUNERATION

Remuneration for tutorials is regulated by ACL policies:
http://aclweb.org/adminwiki/index.php?title=Policy_on_tutorial_teacher_payment

The conversion to euros will be done as follows: €550 for up to 20
people, plus €25 per person for registrants between 21 to 50, plus €18
per person for registrants greater than 50.

Please NOTE: Remuneration for Tutorial presenters is fixed according
to the above policy and does not cover registration fees for the
main conference.

SUBMISSION DETAILS

Proposals for tutorials should contain:

1. A title and brief description of the tutorial content and its
relevance to the ACL community (not more than 2 pages).

2. A brief outline of the tutorial structure showing that the
tutorial’s core content can be covered in a three-hour slot
(excluding a coffee break). In exceptional cases six-hour tutorial
slots are available as well.

3. The names, postal addresses, phone numbers, and email addresses of
the tutorial instructors, including a one-paragraph statement of
their research interests and areas of expertise.

4. A list of previous venues and approximate audience sizes, if the
same or a similar tutorial has been given elsewhere; otherwise an
estimate of the audience size.

5. A description of special requirements for technical equipment
(e.g. internet access). Proposals should be submitted by
electronic mail, in plain ASCII text, to tutorials at eacl2012 dot
org, no later than September 30th 2011.

The subject line should be: “EACL 2012 TUTORIAL PROPOSAL”.

PLEASE NOTE:

– only proposals submitted by e-mail will be taken into account.

– you will receive email confirmation from us that your proposal has
been received. If you do not receive this confirmation 24 hours
after sending the proposal, please contact us personally using both
e.agirre at ehu dot es and lieve.macken at hogent dot be

TUTORIAL SPEAKER RESPONSIBILITIES

Accepted tutorial speakers will be notified by November 3rd, 2011,
and must then provide abstracts of their tutorials for inclusion in
the conference registration material by December 16th, 2011. The
description should be in two formats: an ASCII version that can be
included in email announcements and published on the conference web
site, and a PDF version for inclusion in the electronic proceedings
(detailed instructions to follow).

Tutorial speakers must provide tutorial materials, at least containing
copies of the course slides as well as a bibliography for the material
covered in the tutorial, by February 1st, 2012.

IMPORTANT DATES

Submission deadline for tutorial proposals: October 13th, 2011
Notification of acceptance: November 3rd, 2011
Tutorial descriptions due: December 16th, 2011
Tutorial course material due: February 1st, 2012
Tutorial dates: April 23-24, 2012

TUTORIAL CHAIRS

Eneko Agirre, University of the Basque Country, Spain
Lieve Macken, University College Ghent, Belgium

Please send inquiries concerning EACL 2012 tutorials to:
tutorials at eacl2012 dot org

Researcher position, Statistical Machine Translation

The Machine Learning for Document Access and Translation group of the
Xerox Research Centre Europe conducts research in Statistical Machine
Translation and Information Retrieval, Categorization and Clustering
using advanced machine learning methods. The team is part of the
PASCAL 2 European Network of Excellence, ensuring a strong network of
academic collaboration.

We are opening a position for a researcher with a background in
Statistical Machine Translation to support our participation in the
EU-funded project TransLectures.

Required experience and qualifications:

– PhD in computer science or computational linguistics with focus on
SMT or statistical NLP.
– Good publication record and evidence of implementing systems.
– A good command of English, as well as open-mindedness and the will
to collaborate within a team.
– Acquaintance with Spoken Language Translation is a plus.

Preferred starting date: November 2011

Contract duration: 18 months

Application instructions

Please email your CV and covering letter, with message subject
“Statistical Machine Translation Researcher” to xrce-candidates and to
Nicola.Cancedda at xrce.xerox.com. Inquiries can be sent to
Nicola.Cancedda at xrce.xerox.com.

XRCE is a highly innovative place and we strongly encourage
publication and interaction with the scientific community.

Job announcement URL:

http://www.xrce.xerox.com/About-XRCE/Career-opportunities/Researcher-Statistical-Machine-Translation

CFP: OPT 2011, NIPS Workshop on Optimization for Machine Learning

Dear Colleagues,

We invite participation in the 4th International Workshop on “Optimization for
Machine Learning”, to be held as a part of the NIPS 2011 conference.

Join us for an exciting program that includes plenary talks by:

* Stephen Boyd (Stanford University)
* Aharon Ben-Tal (Technion)
* Ben Recht (UW Madison)

OPT 2011: NIPS Workshop on Optimization for Machine Learning
URL: http://opt.kyb.tuebingen.mpg.de/index.html
Submit: http://www.easychair.org/conferences/?conf=opt2011

Research contributions from the community are welcomed; in particular, we
invite the following two types of submissions:

(i) contributed talks and posters
(ii) open problems

To encourage authors to submit cutting-edge work, the workshop will offer a
best paper award as recognition. We request submitters of open problems to
prepare a few slides that clearly present, motivate, and explain an important
open problem or concern.

The main topics are, including, but not limited to:

* Stochastic, Parallel and Online Optimization,
– Large-scale learning, massive data sets
– Distributed algorithms
– Optimization on massively parallel architectures
– Optimization using GPUs, Streaming algorithms
– Decomposition for large-scale, message-passing and online learning
– Stochastic approximation
– Randomized algorithms

* Nonconvex Optimization,
– Nonsmooth, nonconvex optimization
– Nonconvex quadratic programming, including binary QPs
– Convex Concave Decompositions, D.C. Programming, EM
– Training of deep architectures and large hidden variable models
– Approximation Algorithms

* Algorithms and Techniques (application oriented)
– Global and Lipschitz optimization
– Algorithms for nonsmooth optimization
– Linear and higher-order relaxations
– Polyhedral combinatorics applications to ML problems

* Combinatorial Optimization
– Optimization in Graphical Models
– Structure learning
– MAP estimation in continuous and discrete random fields
– Clustering and graph-partitioning
– Semi-supervised and multiple-instance learning

* Practical techniques
– Optimization software and toolboxes
– GPU, Multicore, Distributed implementations

* Applications close to machine learning
– Sparse learning, compressed sensing, signal processing
– Computational Statistics
– Large scale scientific computing

Important Dates
—————

* Deadline for submission of papers: 26th October 2011
* Notification of acceptance: 12th November 2011
* Final version of submission: 24th November 2011

Please note that at least one author of each accepted paper must be available
to present the paper at the workshop. Further details regarding the
submission process are available at the workshop homepage.

Organizers
———-

* Suvrit Sra, Max Planck Institute for Intelligent Systems
* Sebastian Nowozin, Microsoft Research, Cambridge, UK
* Stephen Wright, University of Wisconsin, Madison

Further Details
—————

http://opt.kyb.tuebingen.mpg.de/index.html

Post-doc position in machine learning for Human-Robot social interaction

The IDIAP Research Institute (www.idiap.ch), a laboratory associated
with EPFL (Swiss Federal Institute of Technology, Lausanne) seeks
qualified candidates for

1 postdoctoral research position in computer vision and machine learning
for multi-modal interaction modeling.

The research will be conducted in the context of the HUMAVIPS
(http://humavips.inrialpes.fr/) project, a 3-year project funded by the
European Community. The overall goal of the project is to endow a
humanoid robot (NAO) with audio-visual sensing and interaction
capabilities allowing him to navigate in a complex environment,
localize a group of people, join it and interact with it.

The research done at Idiap focus on the design of multimodal perceptual
algorithms to recognize human non-verbal behaviors (when people speak,
whom they look at), investigate new approaches to model and identify
people interactions and relationships (e.g. who speaks to whom, who is
the most important person of the group) and exploit the recognized
conversational patterns and relationship in a robot-to-group of people
multimodal interface design.

The postdoctoral researcher will address one of the above theme.
She/he should have a strong background in statistics, applied
mathematics,
and computer vision. Experience in one or several of the following
areas is required:
– computer vision and tracking
– event recognition and discovery
– human-robot or human-computer multimodal interface design
The applicant should be familiar with C/C++ programming and the Linux
environment.
The position offers the opportunity to collaborate with the best
research teams in Europe,
and involvement in PhD supervision.

Contract:
Starting date: as soon as possible.
The initial appointment will be until the end of the project (31/01/2013).

The salary ranges from CHF 70 000 to 75 000/year according to training
and previous experience.

Application:
————–
For further details and application please contact:

Jean-Marc Odobez (odobez(at)idiap.ch, tel : +41 (0)27 721 77 26)

About IDIAP:
————
Idiap is an independent, non-profit research institute recognized and
supported by the Swiss Government, and affiliated with the Ecole
Polytechnique
Fédérale de Lausanne (EPFL).
It is located in the town of Martigny in Valais, a scenic region in the
south of Switzerland, surrounded by the highest mountains of Europe,
and offering exciting recreational activities, including hiking,
climbing and skiing, as well as varied cultural activities.
It is within close proximity to Geneva and Lausanne.
Although Idiap is located in the French part of Switzerland, English is
the working language. Free French lessons are provided.

Idiap offers competitive salaries and conditions at all levels in a young,
dynamic, and multicultural environment. Idiap is an equal opportunity
employer
and is actively involved in the “Advancement of Women in Science” European
initiative. The Institute seeks to maintain a principle of open competition
(on the basis of merit) to appoint the best candidate, provides equal
opportunity for all candidates, and equally encourage both genders to
apply.

Postdoctoral position in computational neuroscience available

University of Surrey

Faculty of Engineering and Physical Sciences

Department of Computing

Research Fellow

Salary: Up to £32751 per annum
(subject to qualifications and experience)

We are seeking a Research Fellow to work on a cross-disciplinary research project in the fields of computational neuroscience, computational systems biology and computational intelligence.

You will work in the Nature Inspired Computing and Engineering (NICE) Group within the Computing Department.

You will have a PhD (or equivalent) in computer science, computational neuroscience, or electrical engineering and a strong interest in computational neuroscience, computational biology and computational intelligence. Expertise in spiking neural network based reservoir computing is essential; knowledge in activity-dependent plasticity or computational modelling of gene regulatory networks is a plus. Programming skills in C/C++ and Matlab is also highly desirable.

Start date: 1st December 2011 or as soon as possible thereafter. The appointment is for 13 months with an extension possibility depending on the availability of further funding.

Informal enquires to Prof. Yaochu Jin (e-mail: yaochu.jin(at)surrey.ac.uk, tel: +44 (0) 1483 686037).

For an application pack and to apply on-line please go to our website: www.surrey.ac.uk/vacancies
If you are unable to apply on-line please contact Mr Peter Li, HR Assistant on Tel:
+44 (0) 1483 686060 or email: K.Li(at)surrey.ac.uk.

Closing date for applications is Friday, 4th November 2011

For further information about the University of Surrey, please visit www.surrey.ac.uk

We acknowledge, understand and embrace cultural diversity

CFP: Deep Hierarchies in Vision

Workshop on Deep Hierarchies in Vision
(in conjunction with CogSys2012 – 5th Int. Conf. on Cognitive Systems)
Webpage: http://www.kovan.ceng.metu.edu.tr/~sinan/DHV/

=> February 21, 2012; Vienna-Austria <= Organizers: Ales Leonardis, Norbert Krueger, Richard Bowden, Sinan Kalkan, Nicolas Pugeault, Frank Guerin Webpage for the 5th Int. Conf. on Cognitive Systems: http://cogsys2012.acin.tuwien.ac.at/ Overview: Processing in the brain in general and visual processing in particular is organized in a hierarchical fashion, from simple localized features to complex, large scale features. The visual system consists of a hierarchy, in which neurons in early visual areas extract simple image features (orientation, motion, disparity) over a small local region of visual space, which are then transmitted to neurons in higher visual areas responding to more complex features (e.g. shape) over a larger region of visual space. Hierarchical representations can derive and organize features at multiple levels. Hierarchical representations with many levels are called 'deep hierarchies'. They build on top of each other by exploiting the shareability of features among more complex compositions or objects themselves. Sharing features, on the one hand, means sharing common computations, which brings about (highly desirable) computational efficiency. On the other hand, reusing the commonalities between objects’ models places their representations in relation to other objects, thus leading to high generalization capabilities and lower storage demands. However, although all neurophysiologic evidence suggests that in the human visual system quite a number of levels are realized, it has turned out that the design and/or learning of such deep hierarchical systems is a very difficult task. Most existing computer vision systems are 'flat' (e.g., having rather simple features - such as SIFT - as input and then applying some kind of SVM learning) and hence cannot make use of the advantages connected to deep hierarchies. Here in particular the generalization capabilities are crucial for any form of cognitive intelligence. As a consequence, we see the issue of establishing deep hierarchies as one major challenge for the establishment of truly cognitive systems. The aim of the workshop is to bring together researchers from vision, robotics, machine learning, artificial intelligence, and neurophysiology to discuss existing obstacles in the design of deep hierarchies, possible solutions as well as perspectives for deep hierarchies in vision and robotics. A special issue with contributions from the workshop on 'Deep Hierarchies in Vision' in a journal is planned. ===================== Invited speakers: ===================== Christian Igel The Image Group, University of Copenhagen, Denmark Justus Piater Department of Electrical Engineering and Computer Science University of Innsbruck, Austria Laurenz Wiskott Institut für Neuroinformatik Ruhr-Universität Bochum, Germany Peter Janssen Division of Neurophysiology Katholieke Universiteit Leuven, Belgium ===================== Submission: ===================== We invite two-page extended abstracts until the 1st of December, 2011. We provide a Latex template for two-page extended abstracts at: http://www.kovan.ceng.metu.edu.tr/~sinan/DHV/. Please follow this template and submit a PDF file online at our workshop website. The submissions will be reviewed by three reviewers, and the decisions will be announced by the 20th of December, 2011. A special issue with contributions from the workshop on 'Deep Hierarchies in Vision' in a journal is planned. ===================== Important Dates: ===================== Abstract Submission: 01.12.2011 (23:59 UTC/GMT) Notification of Acceptance: 20.12.2011 Camera-ready Submission: 15.01.2012 Workshop: 21.02.2012 ===================== Program Co-chairs: ===================== Laurenz Wiskott Institut für Neuroinformatik Ruhr-Universität Bochum, Germany Peter Janssen Division of Neurophysiology Katholieke Universiteit Leuven, Belgium ===================== Contacts: ===================== Ales Leonardis, ales.leonardis(at)fri.uni-lj.si Norbert Krueger, norbert(at)mmmi.sdu.dk Sinan Kalkan, skalkan(at)ceng.metu.edu.tr

Copulas in Machine Learning– NIPS-2011 Workshop — Call for Abstracts

NIPS-2011 Workshop, Granada, Spain December 16th, 2011
http://pluto.huji.ac.il/~galelidan/CopulaWorkshop
DESCRIPTION

From high-throughput biology and astronomy to voice analysis and medical diagnosis,
a wide variety of complex domains are inherently continuous and high dimensional.
The statistical framework of copulas offers a flexible tool for modeling highly non-linear
multivariate distributions for continuous data. Copulas are a theoretically and
practically important tool from statistics that explicitly allows one to separate the
dependency structure between random variables from their marginal distributions.
Although bivariate copulas are a widely used tool in finance, and have even been famously
accused of “bringing the world financial system to its knees” (Wired Magazine, 2009),
the use of copulas for high dimensional data is in its infancy.

While studied in statistics for many years, copulas have only recently been noticed by a
number of machine learning researchers, with this “new” tool appearing in the recent
leading machine learning conferences (ICML, UAI and NIPS). The goal of this workshop is
to promote the further understanding and development of copulas for the kinds of complex
modeling tasks that are the focus of machine learning. Specifically, the goals of the workshop are to:

· draw the attention of machine learning researchers to the important framework of copulas

· provide a theoretical and practical introduction to copulas

· identify promising research problems in machine learning that could exploit copulas

· bring together researchers from statistics and machine learning working in this area

The target audience includes leading researchers from academia and industry, with the aim of facilitating cross fertilization between different perspectives.
CALL FOR ABSTRACTS

We invite submission of abstracts to the workshop. Abstracts will be selected for a short oral or poster presentation.

· Abstracts should be submitted no later than Friday, October 21st, 2011.

· Abstracts should be at most 2 pages long in the NIPS format.

· Abstracts should be submitted by email to galel at huji dot ac dot il
DATES

Submission deadline: October 21st, 2011. Notification of acceptance: November 4th, 2011.
ORGANIZERS

Gal Elidan, The Hebrew University of Jeruslaem
Zoubin Ghahramani Cambridge University and Carnegie Mellon University
John Lafferty, University of Chicago and Carnegie Mellon University

PhD Studentships in Probabilistic Machine Learning

A number of PhD studentships are available in the

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

The group focusses on the development and analysis of scalable Bayesian
inference and graphical modelling technology, with applications to Bayesian
experimental design (adaptive compressive sensing, active learning), Bayesian
learning and robust estimation.
Advances are applied to challenging problems in magnetic resonance imaging
(sampling optimization, motion compensation, phase-sensitive imaging,
parallel MRI, parallel transmit), in collaboration with leading MRI research
groups (CIBM, Lausanne; MPI, Tuebingen), as well as to low-level computer
vision and other high-dimensional scenarios.

The group is part of one of Europe’s highest ranked computer science faculties
at EPFL, one of the leading technical universities worldwide, a unique
surrounding for study and research. EPFL is beautifully located between
Lake Geneva and a stunning mountain scenery, offering great opportunities for
the outdoors. The Lausanne area is known for its numerous cultural festivals.

Openings are available for exceptional students with excellent mathematical
background and 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, 2012

Relevant links:

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

PhD Position in Medical Data Mining: Predicting Response to Therapy in Heart Failure by Using a Multiple Biomarker Panel

Applications are invited for a full-time 4-year PhD research
position shared by the Department of Knowledge Engineering,
Maastricht University, Maastricht, the Netherlands, and the
Heart Failure Clinic of the Maastricht University Medical
Centre, Maastricht, the Netherlands, starting from October-
November, 2011.

The PhD project aims at discovering multiple biomarker panels
from heart-patient data using data-mining techniques. The
final goal is to predict individual outcome and to define
therapy beneficial for individual heart patients.

The PhD student is expected to work on the following main
tasks:
(1) Reviewing, analyzing, and adapting state-of-the-art
data-mining approaches for feature selection, feature
construction, bio-marker discovery, and classification;
(2) Discovering multiple biomarker panels for heart
patients;
(3) Predicting outcome and defining therapy for individual
patients.

Requirements

The PhD student is expected to meet the following requirements:

* A master’s degree (or equivalent) in Computer Science,
Statistics, Data Mining, or a related field, with a
strong interest in feature selection/construction, and
bio-marker discovery;
* Programming skills: Java/C, Matlab;
* An enthusiastic and cooperative working attitude;
* Good didactic abilities.
* Excellent proficiency in written and spoken English.

Conditions of employment

MaastrichtUniversityare set out in the Collective Labour Agreement
of Dutch Universities (CAO). Furthermore, local UM provisions also
apply. For more information look at the website
www.maastrichtuniversity.nl/, A-Z Terms of Employment.

Monthly gross salary during the first year will be: 2,042 euros and
during the fourth year 2,612 euros

Employment basis: 4 years Maximum hours per week: 38

Contract Type: Temporary for 48 months

Organization

Maastricht University (UM) is renowned for its unique, innovative,
problem-based learning system, which is characterized by a
small-scale and student-oriented approach. Research at UM is
characterized by a multidisciplinary and thematic approach, and
is concentrated in research institutes and schools. Maastricht
University has around 14,000 students and 3,500 employees. Reflecting
the university’s strong international profile, a fair amount of both
students and staff are from abroad. The university is comprised of
6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty
of Law, School of Business and Economics, Faculty of Humanities and
Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology
and Neuroscience.

http://www.maastrichtuniversity.nl/

Department

Departement of Knowledge Engineering (DKE)

www.maastrichtuniversity.nl/web/Schools/DKE.htm

Research group BioMathematics and BioInformatics (BMI) within DKE:

www.maastrichtuniversity.nl/web/Schools/DKE/Thema/Research/BMI.htm

Additional information about the vacancy can be obtained from:

Dr. Evgueni Smirnov
Tel.: +31 43 38 82023
Email: smirnov(at)maastrichtuniversity.nl