Machine learning

Fully-funded PhD studentship in constructive machine learning for synthetic engineering of microbial organisms at BIG N2N

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!

Project description:

New BIG N2N partner: Prof. Willem Waegeman

Willem Waegeman is a professor at Ghent University, and a member of the research unit Knowledge-based Systems (KERMIT) of the Department of Mathematical Modelling, Statistics and Bioinformatics. His main interests are machine learning and data science, including theoretical research and various applications in the life sciences. Specific interests include multi-target prediction problems, constructive machine learning and preference learning.

See also

Predictive machine learning methods for species ecology

The study of species' response is a key to understand the ecology of a species (e.g. critical habitat requirement and biological invasion processes) and design better conservation and management plans (e.g. problem identification, priority assessment and risk analysis). Predictive machine learning methods can be used as a tool for modeling species distributions as well as for describing important variables and specific habitat conditions required for a target species.


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