[BBC] PhD studentship in constructive machine learning for synthetic engineering of microbial organisms at Ghent University

[Willem Waegeman]

Fully-funded PhD studentship in constructive machine learning for 
synthetic engineering of microbial organisms at Ghent University

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Duration of studentship: 4 years
Studentship start date: flexible between October 2016 and February 2017

Application closing date: August 15th (will be extended if no suitable 
candidate is found). Apply as soon as possible to avoid disappointment!
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Project description:

Turning organisms into highly-efficient microbial cell factories is a 
daunting process due to the enormous cellular complexity, i.e., the vast 
metabolic network superimposed by a multi-level regulatory layer and the 
fragmentary knowledge thereof. Typically, to steer the strain 
engineering process a “Design-Build-Test” (DBT) cycle is iteratively 
run. However, to move more successfully through this cycle and, hence, 
to more successfully engineer biological systems some major limitations 
need to be overcome:

i)    bottlenecks identified in silico by data-driven and/or model-based 
approaches are often not yielding the expected outcome in vivo 
(“Design”), and
ii)    the DNA parts and engineering tools, originating from synthetic 
biology, perform often inconsistently (“Build”), equally impeding 
reliable biological engineering.

In the proposed project, we aim to solve these limitations by developing 
novel constructive machine learning and synthetic biology tools that 
jointly allow such reliable biological engineering. 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.

Background information:

The studentship is available as a joint initiative between the research 
unit KERMIT under supervision of Prof. Willem Waegeman and the research 
unit MEMO under supervision of Prof. Marjan De Mey.  Both research units 
are part of the Faculty of Bio-science Engineering of Ghent University.

KERMIT (acronym for Knowledge Extraction and Representation Management 
by means of Intelligent techniques) 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.

MEMO (Metabolic engineering of microorganisms) focuses on the 
development of novel tools and methods to fine tune metabolic pathways 
for the biosynthesis of chemically complex metabolites. These novel 
tools and technologies include several DNA parts libraries as well as 
efficient and rapid methods for constructing synthetic pathways, 
transferring them into prokaryotic or eukaryotic microbial systems, and 
screening them in a high-throughput manner. We apply these tools and 
methods to create custom designed microbes for the production of useful 
chemicals from renewable resources, in particular for the production of 
specialty carbohydrates and natural products. These molecules, or their 
direct precursors, have a myriad of high-added value applications in - 
among others- pharmaceuticals, food additives and cosmetics.

The ideal candidate for the position has the following profile:

•An MSc degree in (Bio-)Engineering, Bio-informatics, Computer Science, 
Mathematics,  Statistics,  Physics, or equivalent – candidates from 
outside Belgium are welcome to apply.

•An interest for fundamental machine learning research, as well as 
practical applications in synthetic biology.

•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

•Good knowledge of molecular  and synthetic biology 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, a copy of your MSc.-thesis and/or 
any relevant publications to Ruth Van Den Driessche (ruth DOT 
vandendriessche AT ugent DOT be).