[info] [alife] Research associate position in machine learning at Bristol

[Eugen Leitl]

----- Forwarded message from Titus Brown <titus at caltech.edu> -----

From: Titus Brown <titus at caltech.edu>
Date: Thu, 24 Jan 2008 12:11:48 -0800
To: alife-announce at lists.idyll.org
Subject: [alife] Research associate position in machine learning at Bristol
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Reply-To: kovacs at compsci.bristol.ac.uk

----- Forwarded message from Tim Kovacs <kovacs at compsci.bristol.ac.uk> -----

From: Tim Kovacs <kovacs at compsci.bristol.ac.uk>
Subject: Research associate position in machine learning in Bristol


Research Associate
Department of Computer Science

You will act as RA on the Adaptive Dynamic Ensemble Prediction Techniques 
(ADEPT) project which aims to merge theory and practice between two 
sub-fields of Machine Learning: Ensemble methods and evolutionary Learning 
Classifier Systems. Though they have developed largely in isolation we 
believe these areas are both significantly overlapping and complementary. We 
aim to unify the two areas and use the best of both to develop novel 
predictor-independent methods applicable to a range of machine learning 
problems. This is an exciting opportunity to conduct original scientific 
research at the cutting edge of machine learning from within a large and 
diverse group. You will deliver empirical results, software, theoretical 
analyses, and oral and written reports.

You will be based with the department of computer science's Machine Learning 
and Biological Computation group 
http://www.cs.bris.ac.uk/Research/MachineLearning/ which is part of 
Intelligent Systems at Bristol http://intelligentsystems.bristol.ac.uk/ and 
home to an MSc Machine Learning and Data Mining degree 
http://www.cs.bris.ac.uk/handbook/2007-8/course-msc-cs.html. You will 
collaborate with other project members in Bristol, Manchester and 
Birmingham, and generally contribute to the academic life of the department.

Objectives of the project include:
- Developing ensemble methods for dynamic / non-stationary problems
- Developing ensemble methods for multi-step problems
- Adapting the explicit probabilistic modelling approach of Estimation of
   Distribution Algorithms (EDAs) to our novel methods to better solve 
   problems
   with interactions between variables
- Building a statistical framework for classifier systems by drawing on the
   pattern recognition literature

Essential qualifications:
- A PhD in a relevant area
- An excellent track record of publishing high-quality research
- Excellent communication skills (oral and written)
- Initiative and independence
- A strong background in machine learning, pattern recognition and/or
   evolutionary computation.

Desirable qualifications:
- Evidence of the ability to carry out research independently and to a high
   standard
- A background in one or more of the following:
    - evolutionary computation
    - reinforcement learning
    - ensemble methods
    - multi-objective learning methods
    - probabilistic modelling approaches
    - evolutionary learning classifier systems
- Good software engineering skills (including e.g. familiarity with UML and
   design patterns and experience with substantial projects)

Salary up 28,290 UKP depending on qualifications and experience

Contact for informal enquiries: http:/www.cs.bris.ac.uk/~kovacs

Timescale of appointment:
Contract: Fixed Term Contract
           35 months from start date
Anticipated interview date: 03 March 2008
Anticipated start date:  01 April 2008

Closing date for applications: 9:00am on 18 February 2008

For further information see http://www.bris.ac.uk/boris/jobs/ads?ID=71287

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-- 
Eugen* Leitl <a href="http://leitl.org">leitl</a> http://leitl.org
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