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 – 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@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