Vaga de Doutorado

Dear all,

please feel free to distribute the following vacancy among interested

Fully funded PhD studentship in constructive machine learning for the
life sciences at Ghent University

Duration of studentship: 4 years
Studentship start date: September 2015

Application closing date: July 1st (will be extended if no suitable
candidate is found). Apply as soon as possible to avoid disappointment!

Project description:

Constructive machine learning describes a class of related machine
learning problems where the ultimate goal of learning is not to find a
good model of the data but instead to find one or more particular
instances of the domain which are likely to exhibit desired properties.
While traditional approaches choose these domain instances from a given
set/databases of unlabeled domain instances, constructive machine
learning is typically iterative and searches an infinite or
exponentially large instance space. Interesting applications in the life
sciences are in the domains of chemistry (e.g. de novo drug design),
biology (e.g. gene design, metabolic path design, RNA polymer design),
food sciences (e.g. generation of novel food recipes or cocktails) and
spatio-temporal modelling (e.g. prediction of spatio-temporal maps that
evolve in time, as in climate analysis and ecology). This project will
focus on the development of novel constructive machine learning methods
with a particular emphasis on large output spaces, streaming data and
decomposition techniques for output spaces.

Background information:

The studentship is available in the research unit KERMIT of Ghent
University (acronym for Knowledge Extraction and Representation
Management by means of Intelligent techniques) under supervision of
Prof. Willem Waegeman. KERMIT is a young interdisciplinary team of
mathematicians, engineers and computer scientists, and it draws upon
intelligent techniques resulting from the cross-fertilization between
the fields of computational intelligence and operations research. The
main focus is on mathematical and computational aspects of relational
structures as knowledge instruments, with emphasis on the fields of
fuzzy set theory and machine learning. KERMIT serves as an attraction
pole for applications in the applied biological sciences, and serves
colleagues in hydrology, ecology, bacterial taxonomy, genome analysis,
integrated water management, geographical information systems, forest
management, metabolic engineering, soil science, bioinformatics, systems
biology, etc.

The ideal candidate for the position has the following profile:

-        An MSc degree in (Bio-)Engineering, Computer Science,
Mathematics, Statistics, Bio-informatics, Physics, or equivalent [UTF-8?]–
candidates from outside Belgium are welcome.
-        An interest in fundamental machine learning research, as well
as practical applications in the life sciences
-        In-depth experience with at least one programming language
(Matlab, R, Python, Java, etc.)
-        An interest for applied mathematics, data management and data
analysis in general
-        Good knowledge of machine learning and statistical methods is a
strong asset
-        Fluent in English (speaking and writing, as demonstrated by
personal texts)
-     Knowledge of Dutch is an asset, but not a must
-        Team player with good communication skills

How to apply:

Send your c.v., a motivation letter and a copy of your MSc.-thesis
and/or any relevant publications to Mrs. Ruth Van Den Driessche

Notícia cadastrada em: 14/05/2015 11:22
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