Job opening: 4-year PhD in the field of data mining, knowledge discovery and web analytics in the STW (Dutch Technology Foundation) funded project "Context-Aware Predictive Analytics" (CAPA), within the expertise group Databases and Hypermedia (DH) of TU Eindhoven.
To meet the early a pplication deadline please apply before November 10 athttp://www.academictransfer.com/11685
Late submissions received after November 10th will be also considered until the position has been filled. Please send late submissions to capa.stw@gmail. com with subject "PhD position (V32.1450)".
=== Project description ===
Web analytics is aimed at understanding behavioral patterns of users of various web-based applications or services in e-commerce, mass-media, and entertainment industries. Accurately predicting the probability of desired actions on the web (product purchases, membership registrations, newsletter subscriptions, software downloads, accessing certain information resources, clicking ad banners) in specific circumstances would enable us to achieve better
personalization and adaptation to diverse customer needs and preferences. The behavior of users may vary depending on the context (e.g. user activity, location, time, access device, weather, holidays) and potentially within the context. Thus, predictions in web analytics are inherently context sensitive, and therefore, complementing the prediction models with context management mechanisms are expected to make them more specialized and predictive analytics decisions for web
applications more accurate. In general, the number of contextual factors that may potentially affect human behavior on the we is enormous and it is hardly possible to capture all of them with a model simpler than the universe itself. Therefore, one of the key challenges is to construct the mechanisms, which would identify, what the (current) context is and how to integrate it into prediction models. Another important aspect to be developed is the mechanism of monitoring the stream of user-related and contextual data over time to signal anomalies and changes in predictive model performance. Taking a broad range of practically relevant issues to address within this project, we aim for developing a complete solution integrating predictive analytics, context awareness and change detection mechanisms and allowing straightforward deployment of project results in web-based applications. The techniques we aim to develop will be tested retrospectively on historical data and deployed in real operational settings and validated externally.
The particularly planned prediction tasks include but are not limited to ad banner relevance scoring, content matching in online advertising, bidding for sponsored search, content based and
collaborative recommendations, and demand prediction for a given time period.
There are several partners in the project:
• Eindhoven University of Technology – research institute where the research is funded. The research team of the project includes experts in predictive analytics, concept drift and context-awareness (dr. Mykola Pechenizkiy, dr. Indre Zliobaite), adaptive hypermedia systems
(Prof. Paul De Bra), pattern mining (dr. Toon Calders), and web analytics (Ir. Guido Budziak).
• Adversitement B.V. - technology user that provides expertise in web analytics and infrastructure facilitating research, deployment and testing of research results in real live settings of different interested companies and organization, i.e. end users. Technical
assistance will be also provided to facilitate smooth validation of the developed techniques in the corresponding case studies.
• End users, including e.g. media companies (Kliknieuw.nl) , e-commerce (ReplaceDirect. nl) and entertainment (MovieMax.eu) . The expertise of the corresponding domain specialists who are also part of the project team can be called upon in relation to the deployment case studies.
Thus, the project provides a rather unique opportunity for continuous field testing supported by the involved partners.
Further information about the project including the essential extracts from the original accepted research proposal can be found athttp://www.win.tue.nl/~mpechen/projects/capa/
=== Requirements ===
As a PhD student your tasks concern the following activities:
• perform scientific research;
• implement research prototypes and test them in live settings;
• present results on international conferences;
• publish results in scientific journals;
• participate in activities of the group and of the department.
We are looking for candidates who meet the following requirements:
• a MSc degree in Computer Science or a closely related field;
• background in data mining, machine learning, predictive modeling, information retrieval or recommender systems is a plus;
• interest in studying context-awareness in predictive modeling;
• good programming skills;
• good communication and writing skills in English.
=== Conditions of employment ===
We offer:
• a full-time temporary appointment for a period of 4 years, with an intermediate evaluation after 10 months.
• a gross salary of € 2,042 per month in the first year, increasing up to € 2,612 per month in the fourth year;
• support for your personal development and career planning including courses, summer schools, conference visits etc.;
• a broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare, savings schemes, and excellent sports facilities).
=== Application procedure ===
The application should consist of the following parts:
• an explanation of your interest in the proposed research field;
• a Curriculum Vitae;
• copies of diplomas together and other relevant certificates;
• a complete list of attended courses and corresponding grades;
• names and contact details of two referees;
• proof of English language skills (if applicable).
Please send us your application before November 10th – the date when we start the selection process - using the 'APPLY NOW' button athttp://www.academictransfer.com/11685
Late submissions received after November 10th will be also considered until the position has been filled. Please send late submissions to capa.stw@gmail. com with subject "PostDoc position (V32.1451)".
=== Additional information ===
More information can be obtained from:
• regarding your application: Mrs. P.J. Evers, HR advisor, e-mail: pzwin@tue.nl
• regarding the project content: dr. M. Pechenizkiy, tel. +31 (0)247 4977, e-mail: m.pechenizkiy@ tue.nl
More information about employer Eindhoven University of Technology (TU/e) on AcademicTransfer.
Direct link to this job opening: http://www.academictransfer.com/11685
To meet the early a pplication deadline please apply before November 10 athttp://www.academictransfer.com/11685
Late submissions received after November 10th will be also considered until the position has been filled. Please send late submissions to capa.stw@gmail. com with subject "PhD position (V32.1450)".
=== Project description ===
Web analytics is aimed at understanding behavioral patterns of users of various web-based applications or services in e-commerce, mass-media, and entertainment industries. Accurately predicting the probability of desired actions on the web (product purchases, membership registrations, newsletter subscriptions, software downloads, accessing certain information resources, clicking ad banners) in specific circumstances would enable us to achieve better
personalization and adaptation to diverse customer needs and preferences. The behavior of users may vary depending on the context (e.g. user activity, location, time, access device, weather, holidays) and potentially within the context. Thus, predictions in web analytics are inherently context sensitive, and therefore, complementing the prediction models with context management mechanisms are expected to make them more specialized and predictive analytics decisions for web
applications more accurate. In general, the number of contextual factors that may potentially affect human behavior on the we is enormous and it is hardly possible to capture all of them with a model simpler than the universe itself. Therefore, one of the key challenges is to construct the mechanisms, which would identify, what the (current) context is and how to integrate it into prediction models. Another important aspect to be developed is the mechanism of monitoring the stream of user-related and contextual data over time to signal anomalies and changes in predictive model performance. Taking a broad range of practically relevant issues to address within this project, we aim for developing a complete solution integrating predictive analytics, context awareness and change detection mechanisms and allowing straightforward deployment of project results in web-based applications. The techniques we aim to develop will be tested retrospectively on historical data and deployed in real operational settings and validated externally.
The particularly planned prediction tasks include but are not limited to ad banner relevance scoring, content matching in online advertising, bidding for sponsored search, content based and
collaborative recommendations, and demand prediction for a given time period.
There are several partners in the project:
• Eindhoven University of Technology – research institute where the research is funded. The research team of the project includes experts in predictive analytics, concept drift and context-awareness (dr. Mykola Pechenizkiy, dr. Indre Zliobaite), adaptive hypermedia systems
(Prof. Paul De Bra), pattern mining (dr. Toon Calders), and web analytics (Ir. Guido Budziak).
• Adversitement B.V. - technology user that provides expertise in web analytics and infrastructure facilitating research, deployment and testing of research results in real live settings of different interested companies and organization, i.e. end users. Technical
assistance will be also provided to facilitate smooth validation of the developed techniques in the corresponding case studies.
• End users, including e.g. media companies (Kliknieuw.nl) , e-commerce (ReplaceDirect. nl) and entertainment (MovieMax.eu) . The expertise of the corresponding domain specialists who are also part of the project team can be called upon in relation to the deployment case studies.
Thus, the project provides a rather unique opportunity for continuous field testing supported by the involved partners.
Further information about the project including the essential extracts from the original accepted research proposal can be found athttp://www.win.tue.nl/~mpechen/projects/capa/
=== Requirements ===
As a PhD student your tasks concern the following activities:
• perform scientific research;
• implement research prototypes and test them in live settings;
• present results on international conferences;
• publish results in scientific journals;
• participate in activities of the group and of the department.
We are looking for candidates who meet the following requirements:
• a MSc degree in Computer Science or a closely related field;
• background in data mining, machine learning, predictive modeling, information retrieval or recommender systems is a plus;
• interest in studying context-awareness in predictive modeling;
• good programming skills;
• good communication and writing skills in English.
=== Conditions of employment ===
We offer:
• a full-time temporary appointment for a period of 4 years, with an intermediate evaluation after 10 months.
• a gross salary of € 2,042 per month in the first year, increasing up to € 2,612 per month in the fourth year;
• support for your personal development and career planning including courses, summer schools, conference visits etc.;
• a broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare, savings schemes, and excellent sports facilities).
=== Application procedure ===
The application should consist of the following parts:
• an explanation of your interest in the proposed research field;
• a Curriculum Vitae;
• copies of diplomas together and other relevant certificates;
• a complete list of attended courses and corresponding grades;
• names and contact details of two referees;
• proof of English language skills (if applicable).
Please send us your application before November 10th – the date when we start the selection process - using the 'APPLY NOW' button athttp://www.academictransfer.com/11685
Late submissions received after November 10th will be also considered until the position has been filled. Please send late submissions to capa.stw@gmail. com with subject "PostDoc position (V32.1451)".
=== Additional information ===
More information can be obtained from:
• regarding your application: Mrs. P.J. Evers, HR advisor, e-mail: pzwin@tue.nl
• regarding the project content: dr. M. Pechenizkiy, tel. +31 (0)247 4977, e-mail: m.pechenizkiy@ tue.nl
More information about employer Eindhoven University of Technology (TU/e) on AcademicTransfer.
Direct link to this job opening: http://www.academictransfer.com/11685
