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Funded PhD position; in Social Recommender Systems; at Faculty of Business and Built Environment ; Tampere University of Technology; in Finland

The Faculty of Business and Built Environment is multidisciplinary by nature. Our research explores questions of sustainable built environment and sustainable, technology-centric competitiveness.

Our research interests in Information and Knowledge Management focus on offering, utilizing and managing information and knowledge in organizations in order to improve their competitive advantage. We seek answers to questions such as how information and/or knowledge is best used by different stakeholders, how it is offered, how it creates value, how it can be improved, and why different challenges and obstacles emerge. We are particularly interested in value creation mechanisms, such as information technologies, data assets, data analytics, work systems, and different types of networks.

Job description:

We are looking for Doctoral Student for a two-year research project “Big Match”.

The research in Big Match focuses on the technology and methodology for social matchmaking and people recommenders. Social matching refers to the use of machine learning, computational network analytics, and visualization-based user interfaces to support users in identifying novel and useful social connections. Our approach to social matching is based on the use of Big Social Data (BSD), that is, social media data and other data on individuals, the content they produce, and their interactions with other actors. The doctoral student will join an interdisciplinary team of researchers that operates both at Tampere University of Technology and University of Tampere. Tekes-funded project on social matching provides the researchers a unique opportunity to interact both with international research community and companies that lead the way in data-driven value creation.

Requirements:

The Doctoral Student candidate must hold an applicable higher university degree in, for example, computer science, information systems, or signal processing.

The selected researcher will focus on the design of social matching algorithms and related data-processing pipelines. The ideal candidate is fluent in implementing machine learning-based application and data products in Python (Pandas, Scikit-learn, TensorFlow and is able to implement web-based prototype data products as Single-Page Applications (Vue.js, REST, D3.js). Moreover, the candidate is able to report the research findings in high-quality conferences and journals. A committed individual who is able to work both independently and as a team member and is interested in applied research activities will enjoy this opportunity to develop their competences and capabilities further.

We offer:

A diversified and challenging research position to work on a hot topic of applied machine learning research inside an interdisciplinary team of young researchers, participation in conferences for research exchange, excellent contacts to partners from research and industry, and an independent organization of your research work.

Salary:

The salary will be based both on the job demands and the employee’s personal performance in accordance with the University Salary System. According to the criteria applied to teaching and research staff, the position of a Doctoral student is placed on the job demand levels 1-4. In addition, employees receive performance-based salary. The starting salary for doctoral students on the University’s payroll is of the order of 2300€/month.

Trial period:

Trial period of four (4) months applies.

For more information, please contact:

Jukka Huhtamäki, email: jukka.huhtamaki@tut.fi, tel. +358 40 585 4771

How to apply:

Applications must be submitted by TUT online application form. The closing date for applications is 23 October 2017 (23:59 EEST (GMT+3)). Applications and all accompanying documentation must be in English and preferably in PDF format.

The application should include:

  • CV
  • a short summary of a project (Github repository, research paper, or Master thesis) that showcases your competences related to the position
  • a statement of research interest

Additional information on attachments to applications.

Deadline: October 23, 2017.

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