[BBC] vacancy PhD student

[Willem Waegeman]

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

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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!
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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 – 
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 
(ruth.vandendriessche at ugent.be).



-- 
Prof. dr. Willem Waegeman
Research Unit Knowledge-Based Systems (KERMIT)
Department of Mathematical Modelling, Statistics and Bioinformatics
Coupure links 653 9000 Ghent, Belgium
Phone:  + 32 9 264 59 87
www.kermit.ugent.be
users.ugent.be/~wwaegemn