Call for papers: Journal of Machine Learning Research , Special Topic on Large Scale Learning – Dedline Extension

With the exceptional increase in computing power, storage capacity and network bandwidth of the past decades, ever growing datasets are collected in fields such as bioinformatics (Splice Sites, Gene Boundaries, etc), IT-security (Network traffic) or Text-Classification (Spam vs. Non-Spam), to name but a few. While the data size growth leaves computational methods as the only viable way of dealing with data, it poses new challenges; specifically, most machine learning algorithms hardly scale up beyond 1,000,000 examples or dimensions.

A special topic of the Journal of Machine Learning Research will be devoted to Large Scale Learning, in the line of the NIPS 2007 and ICML 2008 “Efficient Machine Learning” Workshops, and of the Pascal Challenge on Large Scale Learning (http://largescale.first.fraunhofer.de/)

You are invited to submit your contributions to this special issue. For the sake of a principled and fair evaluation, binary classification algorithms must be assessed on the datasets and along the experimental protocol devised for the Large Scale Learning Challenge. More information about the challenge protocol can be found here: http://largescale.first.fraunhofer.de/instructions/

Important dates

Submission: 5 February 2009 ***NEW***
Decision: 15 March 2009
Final versions: 15 April 2009

Topics of Interest

Topics of interest include:

* Applications to very large scale problems in, e.g., bioinformatics, textcategorization, network data
* Efficient training algorithms, e.g., SVMs solvers
* Learning with a budget, e.g., under strict time or memory constraints.
* Efficient parallelization of machine learning algorithms
* Efficient data structures
* On-line learning algorithms
* Large-scale kernel methods
* Coarse to fine algorithms
* Algorithms making use of new hardware, e.g., GPUs, Xilinx

Submission procedure

Authors are kindly invited to follow the standard JMLR format and submission procedure JMLR submission format, the number of pages is limited to 30. Please include a note stating that your submission is for the special topic on Large Scale Learning.

Guest editors

Soeren Sonnenburg, Fraunhofer Institute FIRST, Berlin, Germany
Vojtech Franc, Fraunhofer Institute FIRST, Berlin, Germany
Elad Yom-Tov, IBM Haifa Research Lab, Haifa, Israel
Michele Sebag, LRI, Orsay, France

Fully Funded PhD Studentship in Systems Biology

British Heart Foundation Fully Funded PhD Studentship in Systems Biology

MATHEMATICAL & STATISTICAL MODELLING OF CYTOKINE RECEPTOR CROSS-REGULATION BY CYCLIC AMP

Supervisors:
Dr. Tim Palmer (t.palmer@bio.gla.ac.uk, Integrative and Systems Biology)
Prof. Mark Girolami (girolami@dcs.gla.ac.uk, www.dcs.gla.ac.uk/inference)

Regulation of the immune/inflammatory responses by interleukin-6 (IL-6)-family cytokines is dictated by the interplay of multiple cytokine-activated signalling cascades and inhibitory regulators designed to prevent excessive receptor activation that can result in disease. The situation is further complicated by the observation that cytokine-activated signalling cascades are negatively controlled by distinct signalling modules such as those initiated by the prototypical intracellular messenger cyclic AMP. Despite its significance, the extensive level of cross-talk observed has not been integrated into coherent models of IL-6 receptor signalling and its regulation.

Objectives
By combining molecular/cell biology with mathematical modelling & statistical inferential approaches, this inter-disciplinary studentship will A) statistically define minimal network structures that accurately describe cytokine signalling pathway kinetics, B) derive a set of plausible mathematical models that can identify the critical parameters controlling inhibitory cross-regulation of gp130 by cyclic AMP, and C) identify new approaches for limiting excessive cytokine signalling associated with inflammatory disorders.

The project provides an exciting opportunity for high-quality doctoral training in mathematical modelling & statistical inferential approaches and their application to increase our understanding of the architecture and dynamics of molecular cell signalling pathways. In addition to contemporary
molecular and cellular biology techniques (mammalian cell culture, RNAi-mediated knockdown, protein analysis), the successful candidate will be trained in mathematical modelling of pathway dynamics as well as Bayesian statistical methods to formally characterize uncertainty in these models.

Candidates should be European Economic Area nationals, have an excellent first degree in a relevant mathematical discipline (Mathematics, Computing Science, Statistics, Engineering, Physics) and be highly motivated in their wish to apply this expertise to biological systems. Candidates with an excellent first degree in Biochemistry, Molecular and Cellular Biology or a related discipline, coupled with additional experience in applying mathematical/statistical methods to biological systems, will also be considered.

The studentship will commence as soon as possible after a suitable candidate is identified. The studentship will carry a stipend of £16,853 in year 1 and increasing to £18,580 in year 3. The studentship also covers the student¹s university fees. The studentship is renewable, subject to
satisfactory annual progress, for up to a total of three years.

Applications must consist of a current CV, contact details of at least two academic referees, evidence of degree performance, and a completed application form from
http://www.gla.ac.uk/postgraduate/howtoapplyforaresearchdegree/

Preliminary email enquiries to Tim Palmer or Mark Girolami are welcomed.

Candidates are encouraged to complete the online application, but also to send their CV and associated documents direct to the Graduate School:

Graduate School of Biomedical and Life Sciences, Bower Building,
University of Glasgow, Glasgow G12 8QQ
Tel: ++44 (0)141-330-5800
Fax: ++44 (0)141-330-6093
E-mail: biograd (a) gla.ac.uk (please type ³BHF Palmer² in the subject box of
E-mails)

Post doctoral position in Machine Learning 2009

Starting: March 2009
Location: Paris, France

TOPICS: machine learning for structured data, social networks, random graphs, graphical models

DESCRIPTION:
The Department Signal and Image Processing (TSI) of Telecom ParisTech (France) is offering a one year post-doctoral position in Machine Learning. The post-doctoral fellow will develop and implement machine learning procedures and statistical techniques for investigating the diffusion of information through small social networks. The context of the study is related to food safety and dietary risks.

REQUIRED QUALIFICATION AND SKILLS:
Candidates will be recruited at the level of a PhD in Mathematics or Statistics. They will have confirmed skills in mathematical modelling, data analysis, statistical or machine learning methods, mathematical programming (Matlab or R), and will be highly motivated for applications to social sciences.

PROJECT TEAM:
The candidate will enjoy a challenging and rewarding working environment, within a top leading laboratory in the field of Information and Communication Theory.
Team members: Stéphan Clémençon (Telecom ParisTech – TSI), Fabrice Rossi (Telecom ParisTech – INFRES), Nicolas Vayatis (ENS Cachan – CMLA), Sandrine Blanchemanche (INRA Unité Met@risk), Akos Rona-Tas (UCSD Dept of Sociology).

INSTITUTION: Department of Signal and Image Processing of Institut Telecom – http://www.tsi.enst.fr/ and Laboratory LTCI UMR Telecom ParisTech/CNRS 5141 – http://www.ltci.enst.fr/

FUNDING: position is funded by a new grant « Futur & Rupture « (Institut Telecom).

NET SALARY: ranging from 2200 to 2700 euros per month depending on past experience

CONTACT: Interested applicants should sent C.V. to Stéphan Clémençon
stephan.clemencon (at) telecom-paristech.fr

Postdoctoral position in Brain Computer Interfaces

Faculty of Social Sciences
Maximum Salary: € 4,374 gross/month
Vacancy number: 24.60.08
Closing date: when filled

Job description
This post-doctoral research position in Artificial Intelligence (AI) has a focus on Brain Computer Interfacing (BCI). Brain Computer Interfacing is a new and rapidly growing multi-disciplinary field at the interface between neuro-science and computer-science. We are seeking a highly-motivated technically able candidate to develop novel signal-analysis techniques, mental tasks and subject-training regimes which will push EEG based BCIs to the next level of performance. Current research projects moving towards this goal include; noise-tagging for evoked response BCIs, imagined music/rhythm BCIs, learning spatial/spectral filters, deconvoling overlapping responses, different modalities (tactile/auditory/visual) in BCI, imagined-tapping.

The successful application will be part of the newly formed Donders Institute for Brain, Cognition and Behaviour at the Radboud University of Nijmegen. This position, funded by the large multi-institution and interdisciplinary SmartMix project, BrainGain (see http://www.braingain.nl for details), will study new signal analysis techniques and BCI tasks. Facilities and tools to support these studies include real-time motion tracking systems, eye trackers, visual displays, and a vestibular platform. Two state-of the-art MRI systems, whole-head MEG, and an EEG lab are also available.

Requirements
Preference will be given to candidates with a PhD in Cognitive (Neuro) Science, or signal-processing/machine-learning or related fields, who have a strong motivation to do challenging interdisciplinary research. Further the highly inter-disciplinary nature of BCI research requires an applicant with experience of, or a willingness to learn about, a number of different research areas. Specifically the applicant should have experience in some of: EEG, time-domain and frequency-domain EEG analysis techniques, on-line EEG analysis, evoked and induced response BCIs, neural-correlates of mental tasks, signal-processing techniques, machine-learning, time-series analysis. Strong programming skills, and a demonstrable ability with MatLab is highly desirable.

The applicant will also be expected to help in the design and teaching of BCI courses in the department and supervision of interns (BSc and MSc theses), and PhD students. Further the applicant should have demonstrable experience in conducting scientific research and writing, good collaboration and communication skills. Finally, the application should be able to work in a complex organisation with many partners (Braingain). Pragmatic and productive attitude.

Organization
The CAI offers excellent facilities for PhD students and postdocs. It has its own computer support group, electrotechnical and mechanical technicians, secretarial support, and a renowned scientific staff. The CAI is part of the Donders Institute for Brain, Cognition and Behaviour, which is an outstanding research facility on Cognitive Neuroscience. Nijmegen is the oldest city of the Netherlands, with an interesting history dating back to the Roman Empire, nice surrounding scenery (rivers, hills, woods) and a rich cultural life.
Website: http://www.ru.nl/fsw

Conditions of employment
Maximum employment: 1,0 fte
Maximum salary per month, based on a fulltime employment: € 4,374 gross/month
Salary scale: 11
Duaration of the contract: 3 years.

Other Information
Interested applicants should send CV, statement of background and interests, and names of 2 referees. Review of applications will begin December 31, 2008, and will continue until the position is filled.

Additional Information
dr. Jason Farquhar
Telephone: (+31)024-3611938
E-mail: j.farquhar (at) donders.ru.nl

prof. dr. P. Desain
Telephone: (+31)024-3615885
E-mail: p.desain (at) donders.ru.nl

Application
You can apply for the job (mention the vacancy number 24.60.08) before 27 December 2008 by sending your application -preferably by email- to:

Faculty of Social Sciences/P&O
P.O. Box 9104
6500 HE NIJMEGEN
E-mail: vacancies (at) socsci.ru.nl

3rd Russian Summer School in Information Retrieval (RuSSIR 2009) – Call for Course Proposals

3rd Russian Summer School in Information Retrieval (RuSSIR 2009)
Friday September 11 – Wednesday September 16, 2009
Petrozavodsk, Russia
http://romip.ru/russir2009/

FIRST CALL FOR COURSE PROPOSALS

The 3rd Russian Summer School in Information Retrieval will be held September 11-16, 2009 in Petrozavodsk, Russia. The school is co-organized by the Russian Information Retrieval Evaluation Seminar (ROMIP, http://romip.ru/), Petrozavodsk State University (http://petrsu.ru/), and Karelian Research Centre of the Russian Academy of Sciences (http://www.krc.karelia.ru/). The first and second RuSSIRs took place in Ekaterinburg in 2007 and Taganrog in 2008, respectively (see http://romip.ru/russir2007/ and http://romip.ru/russir2008/). Both events were very successful.

Petrozavodsk, the capital of the Republic of Karelia, was founded in 1703. It is a large industrial and cultural center of the Russian North-West. Petrozavodsk is situated on the shores of Onega Lake, one of the biggest inner lakes in Europe. Karelia is often called “stony lake-forest land” and “the lungs of Europe”, highlighting beautiful landscapes created by countless lakes and rivers and the forest covered land. Petrozavodsk is 400 km away from Saint-Petersburg, an overnight train journey from Saint-Petersburg takes about eight hours. Petrozavodsk State University was founded in 1940 and belongs to the largest educational institutions in the European North of Russia. The university comprises 82 chairs and employs 3,600 faculty/staff members. The total enrollment is more than 19,000 students. IT education and research are one of the main specializations at the university. The Regional Center for New Information Technologies (RCNIT) of PetrSU was the cradle of computer technologies in Karelia and celebrates its 50th anniversary in 2011. PetrSU teams have made remarkable achievements in international student programming
contests.

The target audience of the Summer School is advanced graduate and PhD students, post-doctoral researchers, academic and industrial researchers, and developers. The mission of the school is to teach students about a wide range of modern problems and methods in Information Retrieval; to stimulate scientific research in the field of Information Retrieval; and to create an opportunity for
informal contacts among scientists, students and industry professionals. The Russian Conference for Young Scientists in Information Retrieval will be co-organized with the school. RuSSIR2009 will offer 4 or 5 one-week courses and host approximately 100 participants. The working languages of the school are English (preferable) and Russian.

RuSSIR 2009 is co-located with the yearly ROMIP meeting (http://romip.ru/) and Russian Conference on Digital Libraries 2009 (http://rcdl2009.krc.karelia.ru/).

The RuSSIR2009 Organizing Committee invites proposals for courses on a wide range of IR-related topics, including but not limited to:
– IR theory and models
– IR architectures
– algorithms and data structures for IR
– text IR
– multimedia (incl. music, speech, image, video, etc.) IR
– natural language techniques in IR tasks
– user interfaces for IR
– Web IR (including duplicate detection, hyperlink analysis, query log
processing)
– text mining, information and fact extraction
– mobile applications for IR
– dynamic media IR (blogs, news, WIKIs)
– social IR (collaborative filtering, tagging, recommendation systems)
– IR evaluation.

Each course should consist of five 90-minute-long sessions (normally in five subsequent days). The course may include both lectures and practical exercises in computer labs. A course proposal must contain a brief description of the course (up to 200 words), preferred schedule, prerequisites, equipment needs, a short description of teaching/research experience and contact information of
the lecturer.

RuSSIR2009 organizers will cover travel expenses and accommodation at the school. Lecturers are not paid for their contribution. Details of reimbursement will be negotiated with each lecturer individually. The RuSSIR organizers would highly appreciate if, whenever this is possible, lecturers could find alternative funding to cover travel and accommodation expenses and indicate this possibility in the proposal.

All proposals will be evaluated by the RuSSIR2009 program committee according to the school goals, presentation clarity, lecturer’s qualifications and experience. Topics not featured at previous RuSSIRs are preferred.

Anyone interested in lecturing at RuSSIR2009 is encouraged to submit proposal by email to Pavel Braslavski (pb (at) yandex-team.ru), by January 31, 2009. All submitters will be notified by February 20, 2009 about selection results.

Open positions in Machine Learning, Lille (France)

We would like to advertise that tenure positions for researchers will be opened soon by the French National Research Institute for Computer Science and Control (INRIA, http://www.inria.fr/index.en.html).

In Lille, two research groups have stong interest in machine learning
* Sequel (http://sequel.futurs.inria.fr/), reinforcement learning
* Mostrare (http://mostrare.futurs.inria.fr/), structured prediction
4 positions of junior researchers and 1 position of experienced researcher will be open in Lille.
We would also like to mention that opportunities exist for:
* tenure positions for senior researchers in order to create a new research group
* five years positions for senior researchers
* postdoctoral positions
* PhD grants

A thorough description of these opportunities is given on our Web sites.

If you have any question, please get in touch with us, either remi.gilleron (at) inria.fr (Mostrare), or remi.munos (at) inria.fr, philippe.preux (at) inria.fr for SequeL.
If you want to apply, it is crucial that you get in touch with us, as early as possible.

Workshop on Advances in Machine Learning for Computational Finance: Call for Contributions

Workshop on Advances in Machine Learning for Computational Finance http://web.mac.com/davidrh/AMLCF09/

Sponsored by the PASCAL 2 Network of Excellence http://www.pascal-network.org/

CALL FOR CONTRIBUTIONS: We solicit submissions for the Advances in Machine Learning for Computational Finance workshop to be held on July 20-21, 2009 at University College London Bloomsbury Campus, London, U.K. Computational finance is a cross-disciplinary field which relies on mathematical finance, numerical methods and computer simulation to make trading, hedging and investment decision, as well as facilitating the risk management of these decisions. Machine learning is concerned with the design and development of algorithm and techniques that extract rules and patterns out of data automatically, by computational and statistical methods.

This workshop brings together researches from machine learning, computational finance, academic finance and the financial industry to discuss problems in finance where machine learning may solve challenging problems and provide an edge over existing approaches. The aim of the workshop is to promote discussion on recent progress and challenges as well as on methodological issues and applied research problems. The emphasis will be on practical problem solving involving novel algorithmic approaches.

Topics of the workshop include (but not limited to):

. *Optimisation methods
. *Reinforcement learning
. *Supervised and semi-supervised learning
. *Kernel methods
. *Bayesian estimation
. *Wavelets
. *Evolutionary computing
. *Recurrent and state space models
. *SVM’s
. *Neural networks
. *Boosting
. *Multi-agent simulation
. *….
. *High frequency data
. *Trading strategies and hedging techniques
. *Execution models
. *Forecasting
. *Volatility
. *Extreme events
. *Credit risk
. *Portfolio management and optimisation
. *Option pricing
. *…

The workshop is a core event of the PASCAL 2 EU Network of Excellence.

SUBMISSION PROCEDURE: We invite the submission of high quality extended abstracts (2 to 4 pages) in the NIPS style (http://nips.cc/PaperInformation/StyleFiles). Abstracts should be sent (in .pdf/.ps/.doc) to the organisers (D.Hardoon@cs.ucl.ac.uk , l.zangeneh@cs.ucl.ac.uk). A selection of the submitted abstracts will be accepted as either an oral presentation or poster presentation.

IMPORTANT DATES:

23 Feb 09 – Submission deadline for extended abstracts
30 Mar 09 – Notification of acceptance
20-21 Jul 09 – Workshop at UCL, London, U.K.

CONFIRMED INVITED PASCAL SPEAKERS:
David Cliff
University of Bristol

Vince Darley
Eurobios

Vasant Dhar
New York University, Stern School of Business

László Györfi
Budapest University of Technology and Economics

Michael Kearns
University of Pennsylvania

David Leinweber
University of Berkeley, Haas School of Business

ORGANISERS

David R. Hardoon – University College London
John Shawe-Taylor – University College London
Philip Treleaven -University College London
Laleh Zangeneh – University College London

PROGRAM COMMITTEE

Nicolò Cesa-Bianchi – Università degli Studi di Milano
Ran El-Yaniv – Technion – Israel Institute of Technology
Samet Gogus – Barclaycard
Yuri Kalnishkan – Royal Holloway, University of London
Jasvindor Kandola – Merrill Lynch
Donald Lawrence – University College London
Giuseppe Nuti – Deutsche Bank
Sandor Szedmak – University of Southampton
Chris Watkins – Royal Holloway, University of London

Master/PhD position at INRIA Grenoble / ETH Zurich

The LEAR research group at INRIA Grenoble and the CALVIN research group at the Computer Vision Laboratory of ETH Zurich are looking for a Master and/or PhD student. The candidate will be jointly supervised and will spend time in both institutions (http://lear.inrialpes.fr/ and http://www.vision.ee.ethz.ch/).

Topic: Exploiting associations between text and images will become more and more important over the next few years to reduce the amount of manual annotation necessary to learn visual concepts. Existing work has mainly focused on associating either nouns to image regions [1], or names to faces [2,3]. While techniques for associating nouns to regions require annotated image-nouns pairs, works on names and faces use uncontrolled News captions collected from the internet. However, their success depends heavily on the availability of a pre-trained face detector. In the case of general object classes, such detectors are a central component of what the system should learn automatically. The main goal of this project is to generalize existing approaches so that generic object classes can be learned from image-caption pairs mined from the internet. A possible research avenue is to devise techniques for bootstrapping background knowledge from supervised data, and then automatically move up to less and less supervision. Another important direction is to go beyond individual nouns and explore relations between multiple words, especially words of different types, such as nouns-adjectives and names-verbs. The visual counterparts of adjectives and verbs are attributes [5,6,7] and poses/actions [8,9] respectively. Relational words such as prepositions and comparators [4] could also be incorporated, as well as larger structures composed of more than two words. The multi-entity nature of the project also opens the door to the exciting possibility of automatic learning context models. The project is part of a larger research endeavor to model the parallel between the structure of visual scenes and the structure of natural sentences.

Your profile:
* Bachelor/Masters degree (preferably in Computer Science or Applied Mathematics; Electrical Engineering will also be considered)
* Solid programming skills; the project involves programming in Matlab and C++
* Solid mathematics knowledge (especially linear algebra and statistics)
* Creative and highly motivated
* Fluent in English, both written and spoken
* Prior knowledge in the areas of computer vision, machine learning or data mining is a plus (ideally a Bachelor/master thesis in a related field)

Duration: 6 to 9 month (Masters) or 3 years (PhD)

Start date: As soon as possible

Location: This is a joint project between INRIA Grenoble and ETH Zurich. The candidate will be required to spend time in both institutions.

Contacts:
Res. Dir. Cordelia Schmid, schmid (at) inrialpes.fr
Prof. Vittorio Ferrari, ferrari (at) vision.ee.ethz.ch

Please send applications via email, including:
* a complete CV
* graduation marks
* topic of your Bachelor/master thesis
* the name and email address of two references (including your BS/master thesis supervisor)
* if you already have research experience, please include a publication list and references

Literature:
[1] K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan, Matching Words and Pictures, JMLR 2003
[2] T. Berg, A. Berg, J. Edwards, M. Maire, R. White, Y. Teh, E. Learned-Miller, D. Forsyth, Names and Faces in the News, CVPR 2004
[3] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, Automatic Face Naming with Caption-based Supervision, CVPR 2008
[4] A. Gupta and L. Davis, Beyond Nouns: Exploiting Prepositions and Comparators for Learning Visual Classifiers, ECCV 2008
[5] V. Ferrari and A. Zisserman, Learning Visual Attributes, NIPS 2007
[6] K. Yanai and K. Barnard, Image Region Entropy: A Measure of “Visualness” of Web Images Associated with One Concept, ACM Multimedia 2005
[7] J. Van de Weijer, C. Schmid, and J. Verbeek, Learning Color Names from Real-World Images, CVPR 2007
[8] V. Ferrari, M. Marin-Jiminez, and A. Zisserman, Progressive Search Space Reduction for Human Pose Estimation, CVPR 2008
[9] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, Learning Realistic Human Actions from Movies, CVPR 2008.

PhD Scholarship in Non-Invasive BCI at EPFL

EPFL, one of the two Swiss Federal Institutes of Technology (http://www.epfl.ch), has immediate openings for four PhD students in the field of brain-computer interaction (BCI) to work in the lab of Prof. José del R. Millán (http://people.epfl.ch/jose.millan). Millán’s lab conducts research on non-invasive BCI and neuroprosthetics. His lab is part of the newly-launched Center for Neuroprosthetics (http://actualites.epfl.ch/presseinfo-com?id=661&newlang=eng), which carries out research at the interface of neuroscience and bioengineering in an environment of both theoretical and experimental research, rich for the development of novel enabling technologies as well as for seeking deeper understanding of fundamental mechanisms underlying the field of neuroprosthetics.

The successful candidates will work in the framework of European and Swiss projects related to the development of novel non-invasive BCI for multimodal interaction. They will also investigate the underlying machine learning and signal processing principles for robust recognition of different cognitive processes.

The candidates should have a masters degree (or equivalent) in computer science, electrical engineering, biomedical engineering, or related fields. She or he should have good background in statistical machine learning, signal processing, EEG analysis, and/or human-machine interaction, and/or intelligent/adaptive robotics. Excellent programming skills are a must. Candidates must also have a good command of spoken/written English language. The positions are available immediately and will remain open until suitable candidates are found. Starting date is February 1, 2009, or at the earliest convenience afterwards.

Application: Interested candidates should send a short letter of motivation, a detailed CV, and names of 3 references to Prof. José del R. Millán (jose.millan AT epfl.ch).

Postdoc Openings in Non-Invasive BCI at EPFL

EPFL, one of the two Swiss Federal Institutes of Technology (http://www.epfl.ch), has immediate openings for one postdoc in the field of brain-computer interaction (BCI) to work in the lab of Prof. José del R. Millán (http://people.epfl.ch/jose.millan). Millán’s lab conducts research on non-invasive BCI and neuroprosthetics. His lab is part of the newly-launched Center for Neuroprosthetics (http://actualites.epfl.ch/presseinfo-com?id=661&newlang=eng), which carries out research at the interface of neuroscience and bioengineering in an environment of both theoretical and experimental research, rich for the development of novel enabling technologies as well as for seeking deeper understanding of fundamental mechanisms underlying the field of neuroprosthetics.

The successful candidates will work in the framework of European and Swiss projects related to the development of novel non-invasive BCI for neuroprosthetics. They will also investigate the underlying machine learning and signal processing principles for robust recognition of different cognitive processes.

The candidates should have a PhD in computer science, electrical engineering, biomedical engineering, cognitive neuroscience or related fields. She or he should have good background in statistical machine learning, signal processing, EEG analysis, human-machine interaction, and/or intelligent/adaptive robotics. Excellent programming skills are a must. Candidates must also have a good command of spoken/written English language. The positions are available immediately and will remain open until a suitable candidate is found. Starting date is February 1, 2009, or at the earliest convenience afterwards.

Application: Interested candidates should send a short letter of motivation, a detailed CV, and names of 3 references to Prof. José del R. Millán (jose.millan AT epfl.ch).