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Reference : UMR7522-NATHAM-001
Workplace : STRASBOURG
Date of publication : Friday, June 19, 2020
Scientific Responsible name : M. Stefano BIANCHINI
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2020
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly
Description of the thesis topic
Title of the PhD project: Diffusion and impact of AI in the scientific system
The recent evolution of statistical techniques, the explosion in the computational capabilities of GPUs and the exponential amount of accessible data have contributed to the large-scale diffusion of Artificial Intelligence (AI) in the society. The flexibility of handling various data, structured and unstructured, has significantly increased the use of these techniques in science (Agrawal et al.2018; Bianchini et al., 2020; Brynjolfsson and McAfee 2014). Artificial intelligence can be perceived by the researcher as a tool that expands the knowledge frontier, enabling more distant or previously unused information/knowledge to be combined and boosting innovations. This property of facilitating new breakthroughs has given AI the appellative "invention in the method of inventions" (Cockburn et al. 2018). The increasing adoption of AI techniques in science calls for the assessment of the changes that they could entail in the process of scientific production and their socio-economic impact.
This project aims to investigate: (i) career trajectories of scientists who have incorporated AI methods in their research and the evolution of their network of collaborations; (ii) the effects that AI can have on the size and interdisciplinarity of scientific teams; and (iii) the geographical location of AI users (i.e. existence and determinants of hubs). The thesis will focus on biomedical sciences, a domain that is characterized by the rapid adoption of AI, which in turn leads to innovations with a strong societal impact (Miotto et al. 2017; OECD 2019). In order to highlight potential structural changes in the scientific production process triggered by artificial intelligence, bibliometric resources available from the open-source platform "PubMed" (publications) and PATSTAT (patents) will be used. Data science techniques will also be deployed to extract information from the Web.
The PhD scholarship is sponsored by the CNRS Mission des Initiatives Transversales et Interdisciplinaires "Défi enjeux scientifiques et sociaux de l'intelligence artificielle" (http://www.cnrs.fr/mi/spip.php?article1497&lang=fr). The policy community is increasingly concerned about the role of AI in economics and science. Several partners have already actively supported this research proposal. They have ensured the possibility of collaboration for data sharing and dissemination actions. We have the support of the following institutions: OECD - Structural Policy Analysis Division, Department of Economics; European Commission - DG Research and Innovation; NESTA - National Endowment for Science, Technology and the Arts.
Agrawal, A., McHale, J., & Oettl, A. (2018). Finding needles in haystacks: Articial intelligence and recombinant growth (No. w24541). National Bureau of Economic Research.
Bianchini, S., Muller, M. & Pelletier, P. (2020). Deep learning in science. Mimeo.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National Bureau of Economic Research.
Furman, J., & Seamans, R. (2018). AI and the economy (No. w24689). National Bureau of Economic Research.
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
OECD (2019). Artificial Intelligence in Society.
BETA – Bureau d'Economie Théorique et Appliquée (http://www.beta-umr7522.fr/?lang=fr) is a joint research unit of the CNRS, the Universities of Strasbourg and Lorraine, INRA and AgroParisTech. BETA is located on five sites: Strasbourg, Nancy, Metz, Colmar and Mulhouse. Since the inception of the lab, the research conducted at BETA has been guided by the wish to articulate the theoretical aspects and applications of research in economics and management.
Pursuing excellence in its multiple areas of specialization, BETA is particularly attentive to the training and supervision of doctoral students through the Augustin Cournot Doctoral School (http://ed.ecogestion-cournot.unistra.fr). Beneficiaries of multiple research contracts with public authorities and private partners, BETA members target their work to the scientific community, but also to policy-makers and the general public.
The staff is composed of more than two hundred members, including about a hundred researchers, fifty doctoral students, twenty engineers and administrative personnel. The candidate will be integrated in the CSI (Creativity, Science and Innovation) axis in Strasbourg. This axis promotes research in innovation, entrepreneurship and technological change, as well as the measurement of economic and societal impacts driven by innovation and creativity. Researchers in this axis are well-known for their work on evolutionary economics, economics of science, communities and organizational routines. Ongoing projects focus on the links between digital transformation and productivity, management of intellectual property and creativity.
The candidate must hold a Master's degree in economics/management, mathematics and economics, statistics, mathematics or computer science. The position requires a solid background in data science and econometrics as well as programming skills (R and Python), good oral and written communication skills (English required) to present at conferences and publish articles in scientific peer-reviewed journals.
The candidate will be marginally involved in teaching activities within the Master DS2E Data Science for Economics and Business.
International missions should be planned at least once a year to participate in conferences and/or to present the work in progress.
Applications are to be entered on the job portal CNRS
and should include a detailed CV; at least two references (persons who may be contacted); a one-page motivation letter; a one-page summary of the master thesis. The deadline for sending applications is 20/07/2020 at 5pm.
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