The University of Luxembourg (UL) and Luxembourg Institute of Socio-Economic Research (LISER) invite applications for a DRIVEN PhD Fellow (Doctoral Candidate) position (m/f) as part of the DRIVEN Doctoral Training Unit (https://driven.uni.lu), consisting of 19 doctoral candidates. DRIVEN is funded by the FNR PRIDE funding instrument https://www.fnr.lu/funding-instruments/pride/.
PRIDE PhD Fellow Ref: R-AGR-3440-16-C
You will be working as part of DRIVEN Doctoral Training Unit (DTU) funded by the FNR PRIDE scheme. The Computational and Data DRIVEN Science DTU will train a cohort of 19 Doctoral Candidates who will develop data-driven modelling approaches common to a number of applications strategic to the Luxembourgish Research Area and Luxembourg’s Smart Specialisation Strategies. DRIVEN will build a bridge between state-of-the-art data driven modelling approaches and particular application domains, including Computational Physics and Engineering Sciences, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences.
The global rise of collaborative research in science, technology, engineering, mathematics, and health (STEM+) fields continues to grow exponentially along with scientific production in aggregate. This growth still needs to be mapped for many countries. Completed case studies of selected countries in Europe, North America, East Asia, and the Middle East show how research policies support the continued expansion of higher education and scientific production, but we know less about their specific support for international, intersectoral, and interdisciplinary collaborations. Combining available big datasets (specifically the SPHERE project data based on Clarivate Analytics' Web of Science SCIE) with analysis of research policies and other factors will enable us to identify the drivers of scientific production and different forms of collaborations. In organizational terms, throughout the world, universities are becoming ever more central to contemporary societies, as they vastly increase these countries’ capacity for science. Governments and firms increasingly rely on university-based researchers to create new knowledge, certified by the peer-review process that guides publication of cutting-edge research in thousands of scientific journals. However, the contributions of different organizational forms - from universities and research institutes to firms and government agencies - as well as of individual organizations, demands in-depth analysis. The doctoral candidate would utilize and recode available data to delve below the global aggregates and uncover country, discipline, organizational form, and organizational trends and patterns by exploring network-based methods and data mining techniques.
Your lead supervisor will be Prof. Andreas Zilian. Further supervision will be provided by Prof. Justin Powell, Dr. Jun Pang, and Prof. Stéphane Bordas.
Your primary tasks as a DRIVEN fellow are to:
UL strives to increase the proportion of female PhD students in its faculties. Therefore, we explicitly encourage women to apply.
Before proceeding with the submission of your application, please prepare the following documents.
All documents should be uploaded in PDF format via the online submission system (no applications via email, please). Please note that incomplete applications will not be considered. Deadline is August 23rd,2019
Candidates will be shortlisted based on the criteria detailed above. Shortlisted candidates will be invited for an interview and/or interviewed by phone.
Research work in Luxembourg:
Please see the Foreign Researcher’s Guide to Luxembourg for more information on research employment in Luxembourg and the procedures that apply.
|Title||PRIDE PhD fellow Computational Science|
|Employer||University of Luxembourg|
|Job location||6, rue Richard Coudenhove-Kalergi, L-1359 Luxembourg|
|Published||May 21, 2019|
|Application deadline||August 23, 2019|
|Job types||PhD  |
|Fields||Informatics,   Information Science,   Algorithms,   Artificial Intelligence,   Artificial Neural Network,   Computer and Society,   Computer Architecture,   Computer Communications (Networks),   Computer Graphics,    and 21 more. Cyber Security,   Computing in Mathematics, Natural Science, Engineering and Medicine,   Computing in Social science, Arts and Humanities,   Data Mining,   Data Structures,   Databases,   Distributed Computing,   Human-computer Interaction,   Information Systems (Business Informatics),   Operating Systems,   Parallel Computing,   Programming Languages,   Quantum Computing,   Software Engineering,   Theory of Computation,   Computational Sciences,   Game Design,   Big Data,   Machine Learning,   Machine Vision,   Computer Vision  |