PhD/Postdoc vacancy: Development of deep learning algorithms for the identification of pathogenic species from single-cell MALDI-TOF MS spectra

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Duration of studentship: initially one year, extendable in case of a positive evaluation
Start date: flexible between October 2019 and January 2020

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:

Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS) is a well-known technology, widely used in species identification. Specifically, MALDI-TOF-MS is applied on samples that usually include bacterial cells, generating representative spectra for the various bacteria. In traditional MALDI-TOF MS systems, amplification of the bacterial load is performed by a culturing phase. In this project we will apply MALDI-TOF MS to individual bacterial cells, circumventing the need for culturing and isolation, accelerating the whole process. To this end, a large dataset with more than 50,000 spectra is collected by the company BioSparQ. In the next step we intend to develop novel machine learning algorithms to analyze this data and perform single-cell identification. Due to the large size of the training dataset and the complexity of detecting patterns in MALDI-TOF MS data, we believe that deep learning techniques will be particularly useful in this context.

Background information:

The vacancy is available as a joint initiative between the research unit KERMIT of Ghent University (Belgium), under supervision of Prof. Willem Waegeman, and the company BioSparQ (The Netherlands), under supervision of dr. René Parchen. Both teams have already a collaboration.

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.

BiosparQ B.V. is a privately-held in vitro diagnostics company dedicated to provide healthcare professionals with a unique solution for rapid diagnostics of infectious diseases that improves patient outcomes, lowers healthcare costs and helps turn back. BiosparQ has focused its efforts on research into and the development of the Cirrus D20 medical diagnostic platform, based on proprietary DigiTOFtechnology. This technology enables direct analysis of patient samples using a Single-cell MALDI-TOF approach.

The ideal candidate for the position has the following profile:

· An MSc or PhD 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 microbiology.

· In-depth experience with at least one programming language (Matlab, R, Python, Java, etc.)

· Good knowledge of deep learning methods is an 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.

· This position is available on a PhD and postdoc level, depending on the applications we receive.

· The person hired on this project is expected to travel between Ghent (Belgium) and Leiden (The Netherlands) on a regular basis.

How to apply
Send your c.v., a motivation letter, a copy of your MSc.- or PhD-thesis and/or any relevant publications to Ruth Van Den Driessche (ruth.vandendriessche@ugent.be).

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Prof. dr. Willem Waegeman
Research Unit Knowledge-Based Systems (KERMIT)
Department of Data Analysis and Mathematical Modelling
Coupure links 653 9000 Ghent, Belgium
Phone:  + 32 9 264 92 00
Skype: willem.waegeman 
www.kermit.ugent.be
users.ugent.be/~wwaegemn