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Applications are invited for a fully-funded 4-year PhD scholarship within the Computational Web Intelligence (CWI) lab. CWI is a unit of the Fuzziness and Uncertainty Modelling (FUM) research group of the Department of Applied Mathematics, Computer Science and Statistics (TWIST) at Ghent University. The research will be conducted in cooperation with the Language and Translation Technology Team (LT3) at research group at Ghent University's Department of Translation, Interpreting and Communication.
The scholarship forms part of the project “Fuzzy sets and rough sets for machine learning, sentiment analysis and web intelligence” funded by the Odysseus programme of the Research Foundation-Flanders. Fuzzy sets and rough sets are well-established mathematical AI models designed to deal with imperfections in data: on the one hand, fuzzy sets model vague information, by recognizing that membership to certain concepts, or logical truth of certain propositions, is a matter of degree. On the other hand, rough sets deal with incomplete information by approximating concepts from below and from above, using information granules as building blocks. In the project, we focus on the approximation of concepts using a fuzzy relation expressing degrees of similarity or dominance.
In particular, this PhD scholarship involves the development and evaluation of novel machine learning (ML) techniques to tackle the problems of sentiment analysis (SA) and emotion detection (ED) from textual data. The importance of these domains has increased manifold over the past years thanks to the availability of huge amounts of customer review data, and the relevance of the latter for e-commerce purposes. Your research will build on the results of previous research conducted at CWI and LT3, and will in particular combine fuzzy sets and rough sets with state-of-the-art ML techniques (deep learning, similarity learning, etc.) to address a variety of different tasks in SA/ED, involving challenging data settings such as semi-supervised, multi-label and ordinal classification.
The scholarship’s prospective starting date is October 1, 2019. Interested candidates should e-mail: (i) a detailed curriculum vitae; (ii) a motivation letter; (iii) BSc and MSc transcripts of grades and the MSc thesis, and (iv) two reference letters to Dr. Chris Cornelis (chris.cornelis@ugent.be) and Dr. Veronique Hoste (veronique.hoste@ugent.be) by May 31st, 2019. Indicate “Application: PhD Researcher on machine learning methods for sentiment analysis and emotion detection” in the email subject. Note that all applications will be thoroughly screened in order to satisfy the high scientific standards of our research group. The selection process will take place along June 2019, and will involve a small research task to assess your programming and problem solving skills and an interview (possible by Skype).
Applications are invited for a fully-funded 4-year PhD scholarship within the Computational Web Intelligence (CWI) lab. CWI is a unit of the Fuzziness and Uncertainty Modelling (FUM) research group of the Department of Applied Mathematics, Computer Science and Statistics (TWIST) at Ghent University.
The scholarship forms part of the project “Fuzzy sets and rough sets for machine learning, sentiment analysis and web intelligence” funded by the Odysseus programme of the Research Foundation-Flanders. Fuzzy sets and rough sets are well-established mathematical AI models designed to deal with imperfections in data: on the one hand, fuzzy sets model vague information, by recognizing that membership to certain concepts, or logical truth of certain propositions, is a matter of degree. On the other hand, rough sets deal with incomplete information by approximating concepts from below and from above, using information granules as building blocks. Here, we focus on the approximation of concepts using a fuzzy relation expressing degrees of similarity or preference.
In particular, the PhD scholarship involves harnessing and combining a variety of machine learning (ML) techniques (deep learning, similarity learning, etc.) and data settings (big data, semi-supervised, multi-instance, multi-label, etc.) with improved or novel fuzzy rough set models. It will build on the results of previous research conducted at CWI, and at the same time, will reach out to (aspect-based) sentiment analysis as a test bed for the developed methods.
The scholarship’s prospective starting date is October 1, 2018. Interested candidates should e-mail:
to Dr. Chris Cornelis (Chris.Cornelis@UGent.be) and Dr. Daniel Peralta (Daniel.Peralta@UGent.be) by May 31st, 2018. Indicate “Application: PhD Researcher on fuzzy rough set models for machine learning and sentiment analysis” in the email subject.
Note that all applications will be thoroughly screened in order to satisfy the high scientific standards of our research group. The selection process will take place along June 2018. As part of this process, potential candidates will have to conduct a small research task to assess their programming and problem solving skills, and will be invited for an interview (possible by Skyp
Applications are invited for a fully-funded 4-year PhD scholarship within the Computational Web Intelligence (CWI) lab. CWI is a unit of the Fuzziness and Uncertainty Modelling (FUM) research group of the Department of Applied Mathematics, Computer Science and Statistics (TWIST) at Ghent University. The research will be conducted in cooperation with the Laboratory of Intelligent Decision Support Systems (IDSS), an organizational unit within the Institute of Computing Science of the Poznań University of Technology.
The scholarship forms part of the project “Fuzzy sets and rough sets for machine learning, sentiment analysis and web intelligence” funded by the Odysseus programme of the Research Foundation-Flanders. Fuzzy sets and rough sets are well-established mathematical AI models designed to deal with imperfections in data: on the one hand, fuzzy sets model vague information, by recognizing that membership to certain concepts, or logical truth of certain propositions, is a matter of degree. On the other hand, rough sets deal with ambiguous information by approximating concepts, using information granules as building blocks. Here, we focus on the approximation of fuzzy concepts, exploiting the monotonic relationship between membership degrees of some features and the membership to decision classes.
In particular, the PhD scholarship involves the extension of the dominance based rough set approach (DRSA) by means of fuzzy logic, used at the stage of defining rough approximations. These fuzzy-rough approximations are then used in inductive learning of decision rules. The set of decision rules induced in this way is on one hand a preference model and on the other hand a basic classifier in an ensemble used for classification of unseen objects. It will build on the results of previous research conducted at CWI, IDSS and at the Department of Economics and Business of the University of Catania.
Your key responsibilities include:
The scholarship’s prospective starting date is October 1, 2018. Interested candidates should e-mail:
to Dr. Chris Cornelis (Chris.Cornelis@UGent.be) and Prof. Roman Słowiński (Roman.Slowinski@cs.put.poznan.pl) by June 15th, 2018. Indicate “Application: PhD Researcher on dominance based rough set models for preference learning in ordinal classification” in the email subject.
Note that all applications will be thoroughly screened in order to satisfy the high scientific standards of our research group. The selection process will take place along the second half of June 2018. As part of this process, selected candidates will have to conduct a small research task to assess their programming and problem solving skills, and will be invited for an interview (possible by Skype).