Two Lectureships in Statistical Science, University College London

Lecturers in Statistics (2 posts) Ref:1207600

The Department of Statistical Science at UCL is currently undergoing a
programme of expansion. As part of this expansion the Department invites applications
for two Lecturerships in Statistics. The successful applicants will be expected
to carry out original research and to contribute to undergraduate and postgraduate
teaching in Statistical Science.

The successful candidates will have proven records of their ability to
carry out high quality research in a branch of probability or statistics, including
evidence of success in publishing in journals of international repute. The
appointees should have the ability to construct and deliver lecture courses on topics
relevant to the needs of the Department and possess excellent interpersonal, oral and written
communication skills. Applicants should hold, or be about to submit, a PhD in
Statistics or a related discipline.

Both posts are available from November 2011. Salary (inclusive of London
allowance) will be Grade 7 31,905-38,594GBP per annum or Grade 8 39,668-46,822GBP per
annum. Appointments at either grade will also receive an additional market
supplement of 7,000GBP per annum.

Further particulars including a job description and person specification
for the posts can be accessed at at
http://www.ucl.ac.uk/statistics/department/jobs. If you wish to discuss these posts informally please contact the Head of Department, Professor Tom Fearn, email: tom@stats.ucl.ac.uk, tel: +44 (0)20 7679 1873. For any queries regarding the application process please contact Dr Russell Evans, email: russell(at)stats.ucl.ac.uk, tel: +44 (0)20 7679 8311.

Closing Date: 12 Oct 2011

EACL 2012 / NAACL-HLT 2012 / ACL 2012 – CALL FOR WORKSHOP PROPOSALS

CALL FOR WORKSHOP PROPOSALS
EACL 2012 / NAACL-HLT 2012 / ACL 2012
(http://www.ling.helsinki.fi/~kjokinen/WorkshopCFP/)

The European Chapter of the Association for Computational Linguistics
(EACL), The North American Chapter of the Association for Computational
Linguistics (NAACL), and The Association for Computational Linguistics
(ACL) invite proposals for workshops to be held in conjunction with the
EACL, NAACL, or ACL conferences in the spring and summer of 2012. We
solicit proposals on any topic of interest to the ACL communities.
Workshops will be held at one of the following conference venues:

* EACL 2012 is the 13th Conference of the European Chapter of the
Association for Computational Linguistics. It will be held in
Avignon, April 23 – 27, 2012. The dates for the EACL workshops
will be April 23-24. The webpage for EACL 2012 is:
http://eacl2012.org/.

* NAACL-HLT 2012 is the 13th Annual Meeting of the North American
Chapter of the Association for Computational Linguistics. It will
be held Montreal, Canada, June 3 – 8, 2012. The dates for the
NAACL-HLT workshops will be June 7 – 8. The webpage for NAACL-HLT
2012 is: http://www.naaclhlt2012.org/.

* ACL 2012 is the 50th Annual Meeting of the Association for
Computational Linguistics (ACL). It will be held in Jeju, Republic
of Korea, July 8 – 14, 2012. The ACL workshops will be held July
12 – 13. The webpage for ACL 2012 is: http://www.acl2012.org/.

Proposals will be jointly reviewed by the workshop organizers for all
three conferences.

SUBMISSION INFORMATION

Similarly to previous conferences, the submission and reviewing of
workshop proposals for EACL, NAACL-HLT, and ACL will be coordinated.

Proposals for workshops should contain:

1. A title and brief (2-page max) description of the workshop topic
and content.
2. The desired workshop length (one or two days), and an estimate of
the number of attendees.
3. The names, postal addresses, phone numbers, and email addresses of
the organizers, with one-paragraph statements of their research
interests and areas of expertise.
4. A list of potential members of the program committee, with an
indication of which members have already agreed.
5. A description of any shared tasks associated with the workshop.
6. A description of special requirements for technical needs.
7. A note specifying which venue(s) (EACL versus NAACL-HLT versus
ACL) would be acceptable to you; if all are acceptable, you may
express preference for one or the other.

There will be a single workshop committee, coordinated by the three sets
of workshop chairs. This single committee will review the quality of the
workshop proposals. Once the reviews are complete, the workshop chairs
will work together to assign workshops to each of the three conferences,
taking into account the location preferences given by the proposers.

The ACL has a set of policies on workshops. You can find the ACL’s
general policies on workshops at
http://www.cis.udel.edu/~carberry/ACL/Workshops/workshop-support-general-policy.html,
the financial policy for workshops at
http://www.cis.udel.edu/~carberry/ACL/Workshops/workshop-conf-financial-policy.html,
and the financial policy for SIG workshops at
http://www.cis.udel.edu/~carberry/ACL/Workshops/workshops-Sig-financial-policy.html.

* Please submit proposals in plain text in the body of an email to the
workshop organizers:
eacl.naacl.acl.workshops.2012_AT_helsinki_DOT_fi

no later than *October 28, 2011, 23:59:59 UTC/GMT*
(which is 18:59:59 EST, 15:59:59 PST, and 08:59:59 JST on Oct 29).

* Notification of acceptance of workshop proposals will occur no later
than *November 11, 2011*.

Since the three conferences will occur at different times, the
timescales for the submission and reviewing of workshop papers, and the
preparation of camera-ready copies, will be different for each
conference. Suggested timescales for each of the conferences are given
below. Workshop organizers should not deviate from this schedule unless
absolutely necessary.

Workshop organisers are also requested to pay attention to the fact that
there will be only a week between the notification of the workshop
acceptance and sending out the first CFPs for the workshop (in case of EACL
and ACL). Thus it is important that the workshop proposals are already well
structured and organised at the time of the submission, to allow quick launch
of the first CFP.

TIMELINES FOR 2012 WORKSHOPS

* SHARED DATES
Oct 28, 2011 Workshop proposal deadline
Nov 11, 2011 Notification of acceptance

* EACL 2012
Nov 18, 2011 Proposed 1st workshop CFP
Jan 27, 2012 Proposed paper due date
Feb 24, 2012 Proposed notification of acceptance
Mar 09, 2012 Camera-ready deadline
Apr 23-24, 2012 Workshops

* NAACL-HLT 2012
Dec 16, 2011 Proposed 1st workshop CFP
Mar 02, 2012 Proposed paper due date
Mar 30, 2012 Proposed notification of acceptance
Apr 13, 2012 Camera-ready deadline
Jun 7-8, 2012 Workshops

* ACL 2012
Nov 21, 2011 Proposed 1st workshop CFP
Mar 18, 2012 Proposed paper due date
Apr 15, 2012 Proposed notification of acceptance
Apr 30, 2012 Camera-ready deadline
Jul 12-13, 2012 Workshops

WORKSHOP CO-CHAIRS

* EACL 2012
Kristiina Jokinen, University of Helsinki – http://www.ling.helsinki.fi/~kjokinen/
Alessandro Moschitti, University of Trento – http://disi.unitn.it/moschitti/

* NAACL-HLT 2012
Colin Cherry, National Research Council Canada – https://sites.google.com/site/colinacherry/
Mona Diab, Columbia University – http://www1.ccls.columbia.edu/~mdiab/

* ACL 2012
Massimo Poesio, University of Essex – http://cswww.essex.ac.uk/staff/poesio/
Satoshi Sekine, New York University – http://nlp.cs.nyu.edu/sekine/

For inquiries, send email to the workshop organizers:
eacl.naacl.acl.workshops.2012_AT_helsinki_DOT_fi

Nordstat 2012

It gives us great pleasure to invite you to the 24th Nordic Conference in Mathematical Statistics (Nordstat), which will be held in Umeå, June 10-14, 2012.

http://www.nordstat2012.se/

Nordstat is a biennial international meeting for statisticians and probabilists, organized by the Nordic and Baltic countries. The conference is being held at Umeå University.

Umeå is one of the major cities in Northern Sweden and has been appointed the European Capital of Culture for 2014. Umeå is a green city known as the City of Birches. Almost 3000 birch trees were planted along the city’s wide avenues following a devastating fire that destroyed large parts of the city back in 1888. In June the sun barely dips below the horizon, providing almost continuous daylight around the clock.
We welcome you to participate in the scientific and social programme. We hope that you will enjoy Nordstat and take the chance to experience Umeå with its bright beautiful summer nights.

We look forward to see you all in Umeå!

The Local Organizing Committee

CFP: NIPS 2011 Workshop on Discrete Optimization in Machine Learning (DISCML) – Uncertainty, Generalization and Feedback

Call for Papers

3rd Workshop on
Discrete Optimization in Machine Learning (DISCML):
Uncertainty, Generalization and Feedback

at the
25th Annual Conference on Neural Information Processing Systems
(NIPS 2011)

http://www.discml.cc

Submission Deadline: Friday 11/11/11, 11:11

Solving optimization problems with ultimately discretely solutions is
becoming increasingly important in machine learning: At the core of
statistical machine learning is to infer conclusions from data, and
when the variables underlying the data are discrete, both the tasks of
inferring the model from data, as well as performing predictions using
the estimated model are discrete optimization problems. This workshop
aims at exploring discrete structures relevant to machine learning and
techniques relevant to solving discrete learning problems.
The focus of this year’s workshop is on the interplay between discrete
optimization and machine learning: How can we solve inference problems
arising in machine learning using discrete optimization? How can one
solve discrete optimization problems that themselves are learned from
training data? How can we solve challenging sequential and adaptive
discrete optimization problems where we have the opportunity to
incorporate feedback? We will also explore applications of such
approaches.

We would like to encourage high quality submissions of short papers
relevant to the workshop topics. Accepted papers will be presented as
spotlight talks and posters. Of particular interest are new
algorithms with theoretical guarantees, as well as applications of
discrete optimization to machine learning problems in areas such as
the following:

Combinatorial algorithms

* Submodular & supermodular optimization
* Discrete convex analysis
* Pseudo-boolean optimization
* Randomized / approximation algorithms

Continuous relaxations

* Sparse approximation & compressive sensing
* Regularization techniques
* Structured sparsity models

Learning in discrete domains

* Online learning / bandit optimization
* Generalization in discrete optimization
* Adaptive / stochastic optimization

Applications

* Graphical model inference & structure learning
* Clustering
* Feature selection, active learning & experimental design
* Structured prediction
* Novel discrete optimization problems in ML, Computer Vision, NLP, …

Submission deadline: November 11, 2011

Length & Format: max. 6 pages NIPS 2011 format

Time & Location: December 16/17 2011, Sierra Nevada, Spain

Submission instructions: Email to submit(at)discml.cc

Organizers:
Andreas Krause (ETH Zurich, Switzerland),
Pradeep Ravikumar (University of Texas, Austin),
Jeff A. Bilmes (University of Washington),
Stefanie Jegelka (Max Planck Institute for Intelligent Systems, Germany)

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/