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!
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.
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).
Prof. Willem Waegeman