Overview
The aim of the GMTaLe project is to develop a framework for analysing task-relatedness based on an approach combining graphs and non-parametric machine-learning techniques. The framework will be used for improving generalization performance of multi-task learning algorithms and the interpretability of models generated by these algorithms. The project involves one PhD student and one post-doctoral researcher. As a post-doctoral researcher you will work on the following main tasks:
- applying graph theory to analyse how task-relatedness changes when different similarity measures are used;
- developing and analysing graph-based transformations for knowledge transfer;
- demonstrating the practical relevance of the proposed research by applying the approach to real-life case studies;
- integrating the results of the project activities into an open-source graph-based tool for task-relatedness analysis.
Faculty of Science
Maximum Salary: € 3,755 gross/month
Vacancy number: 62.06.10
Closing date: 30 April 2010
Requirements
You should have a PhD degree in Computer Science. A background in machine learning is recommended. In addition, you should have experience with, and a strong interest in, tool development. You are a team player with an open mind and excellent social and organizational skills. You have the ability to operate at various abstraction levels, and you are fluent in English (speaking and writing).
Organization
Radboud University Nijmegen is one of the leading academic communities in the Netherlands. Renowned for its green leafy campus, modern buildings, and state-of-the-art equipment, it has nine faculties and more than 17,500 students enrolled in approximately 90 study programmes. The university is situated in the oldest Dutch city, close to the German border, on the banks of the river Waal. The city has a rich history and one of the liveliest city centres in the Netherlands.
The machine-learning group of the Intelligent Systems Section of the Institute for Computing and Information Sciences (ICIS) at Radboud University Nijmegen conducts research in machine learning and artificial intelligence in general, with applications to neuroscience, bioinformatics, and other fields.
Website: http://www.ru.nl/is/ml/
Conditions of employment
Employment: 1,0 fte
Maximum salary per month, based on a fulltime employment: € 3,755 gross/month
Minimum salary for the postdoctoral position is €2,861 (scale 10.4). Initial salary depends on relevant work experience.
Duration of the contract: you will be appointed as a post-doctoral researcher for a period of two years.
Additional Information
Dr. Elena Marchiori
Telephone: +31 24 36 52647 +31 24 36 52647
E-mail: elenam@cs.ru.nl
Application
You can apply for the job (mention the vacancy number 62.06.10) before 30 April 2010 by sending your application -preferably by email- to:
RU Nijmegen, FNWI, P&O, mrs. D. Reinders
P.O. Box 9010, 6500 GL Nijmegen, NL
Telephone: +31 24 3652027 +31 24 3652027
E-mail: pz@science.ru.nl
Please kindly mention Scholarization.blogspot.com when applying for this opportunity
The aim of the GMTaLe project is to develop a framework for analysing task-relatedness based on an approach combining graphs and non-parametric machine-learning techniques. The framework will be used for improving generalization performance of multi-task learning algorithms and the interpretability of models generated by these algorithms. The project involves one PhD student and one post-doctoral researcher. As a post-doctoral researcher you will work on the following main tasks:
- applying graph theory to analyse how task-relatedness changes when different similarity measures are used;
- developing and analysing graph-based transformations for knowledge transfer;
- demonstrating the practical relevance of the proposed research by applying the approach to real-life case studies;
- integrating the results of the project activities into an open-source graph-based tool for task-relatedness analysis.
Faculty of Science
Maximum Salary: € 3,755 gross/month
Vacancy number: 62.06.10
Closing date: 30 April 2010
Requirements
You should have a PhD degree in Computer Science. A background in machine learning is recommended. In addition, you should have experience with, and a strong interest in, tool development. You are a team player with an open mind and excellent social and organizational skills. You have the ability to operate at various abstraction levels, and you are fluent in English (speaking and writing).
Organization
Radboud University Nijmegen is one of the leading academic communities in the Netherlands. Renowned for its green leafy campus, modern buildings, and state-of-the-art equipment, it has nine faculties and more than 17,500 students enrolled in approximately 90 study programmes. The university is situated in the oldest Dutch city, close to the German border, on the banks of the river Waal. The city has a rich history and one of the liveliest city centres in the Netherlands.
The machine-learning group of the Intelligent Systems Section of the Institute for Computing and Information Sciences (ICIS) at Radboud University Nijmegen conducts research in machine learning and artificial intelligence in general, with applications to neuroscience, bioinformatics, and other fields.
Website: http://www.ru.nl/is/ml/
Conditions of employment
Employment: 1,0 fte
Maximum salary per month, based on a fulltime employment: € 3,755 gross/month
Minimum salary for the postdoctoral position is €2,861 (scale 10.4). Initial salary depends on relevant work experience.
Duration of the contract: you will be appointed as a post-doctoral researcher for a period of two years.
Additional Information
Dr. Elena Marchiori
Telephone: +31 24 36 52647 +31 24 36 52647
E-mail: elenam@cs.ru.nl
Application
You can apply for the job (mention the vacancy number 62.06.10) before 30 April 2010 by sending your application -preferably by email- to:
RU Nijmegen, FNWI, P&O, mrs. D. Reinders
P.O. Box 9010, 6500 GL Nijmegen, NL
Telephone: +31 24 3652027 +31 24 3652027
E-mail: pz@science.ru.nl
Please kindly mention Scholarization.blogspot.com when applying for this opportunity
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