Offer Description

This postdoc position is part of the ERC Starting Grant project “MOOVA-Making Old Objects Valuable Again. The Cultural, Economic Challenges and Sustainability Opportunities of Antiques in the 21st Century,” led by Prof. Anne-Sophie V. Radermecker.

Regardless of their cultural significance, many “ordinary” antiques remain on the margins of cultural institutions and scholarly research. As a result, the question of how everyday antiques are safeguarded, valued, marketed, perceived, and consumed remains largely underexplored, placing material evidence of our visual culture in a critical situation, especially in view of the technological transition faced by this ageing business. Using old objects as a laboratory for transdisciplinary research, MOOVA aims to 1) highlight the complex cultural value of ordinary antiques and the patrimonial role of local antique dealers as safeguarders of informal heritage; 2) explore the supply and demand for antiques in the digital era; 3) understand the perceived value of antiques by Gen Z; and 4) develop theoretical and applied models for reassessing the meaning, relevance, and value of those objects nowadays. MOOVA’s originality lies in its focus (European decorative arts), transversal approach (cross-objects, markets, countries), and combination of theories, data, and methods from various disciplines (heritage studies, cultural economics, cognitive science, etc.). MOOVA's academic outputs will deal with vital issues such as the preservation of local heritage, audience renewal, and local place branding.

The successful candidate will conduct a 3-year postdoc (3x1 year renewable after an annual evaluation). She/he will be attached to the research unit GRESAC (Groupe de recherche en sciences des arts et de la culture – Institute of Sociology), jointly to the CEBRIG (Centre Emile Bernheim de Recherche Interdisciplinaire en Gestion - Solvay Brussels School - Economic & Management).

Duties and responsibilities

The successful candidate will carry out a postdoctoral research on the online market for antiques. In close collaboration with the PI and the MOOVA team, the postdoc will mainly work on the following tasks:

• Collect and analyze art market data (price formation mechanisms, price indices, etc.);
• Develop a conceptual framework to analyze the vertical segmentation of the art market (with a special focus on the low- and middle-end markets);
• Investigate the market structure of the antiques trade (theoretically and empirically);
• Investigate the potential of antiques as an investment (theoretically and empirically);
• Generate innovative ways of dealing with imperfect art market data, develop new models (e.g., regressions, machine learning) to capture the complex idiosyncrasies of antiques and test related assumptions;
• Actively participate in international scientific conferences and workshops in related disciplines;
• Publish in top-ranking international journals in related disciplines (single and co-authored papers) and contribute to the edited volume that will derive from this project;
• Actively assist the PI with administrative and logistical support (team meetings, reporting, workshop, and conference organization, outreach dissemination, etc.).

Candidate’s profile

Excellent skills in data mining and creative thinking are the main requirements for this position. The candidate’s ability to deal with the complexity of art market data and to learn about the economy of the arts will also be essential. We expect the prospective postdoc to have:

• A completed PhD in disciplines involving data science (e.g., economics, finance, statistics, econometrics, maths, computational social sciences, digital humanities). Other disciplines can be considered if relevant;
• Evidence that demonstrates his/her academic excellence in his/her field of specialization;
• Demonstrable knowledge and experience with ETL and statistical packages (e.g., Python; SAS, SPSS, R, Spark);
• Knowledge and experience in programming languages, and supervised machine learning in particular are considerable advantages (e.g., Javascript, Spark, S, C);
• Technical expertise in data preparation, data models, data mining, and segmentation;
• Excellent communication, relational, organization skills and research autonomy.