Information for applicants are available (only in Italian) at the Call for Applications https://pica.cineca.it/unito/assegni-di-ricerca-unito-2022-i/file/Bando%20da%20pubblicare.pdf. Within the Call for Applications, the postdoctoral position appears at page 24 under the title “Statistica Bayesiana non-parametrica e non-parametrica empirica per problemi di campionamento di specie”. Reference code: ESOMAS.2022.01. 

Applications are made only online - https://pica.cineca.it/unito/ - by selecting “Your Applications” in the box “Bando Assegni di ricerca - Tornata I 2022” (code TornataI2022). The application procedure is available in Italian/English, and it requires a CV, two reference letters and a research statement. 

Prospective candidates may contact directly Stefano Favaro - stefano.favaro@unito.it - for any information on the postdoctoral position and the application procedure.

Best wishes

Stefano Favaro

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Prospective candidates are expected to have experience on nonparametric statistics, within the classical (frequentist) and/or Bayesian paradigm, and they should preferably be holding a Ph.D. or being close to receiving one. The research shall be carried out in English.

The duration of the contract is 24 months. Expected starting date in May 2022, but a different date may be arranged. The salary amounts to 46,000 Euros per year, including taxes and social charges, and considerable financial support to attend conferences and workshops will be granted. There are no teaching duties associated to the position.

Abstract. Object of research are species sampling problems, whose importance has grown considerably in recent years driven by numerous applications in the broad area of biosciences, and also in machine learning, theoretical computer science and information theory. Within the broad field of species sampling problems, the research will be focussed on two research themes: i) the study of nonparametric Bayes and nonparametric empirical Bayes methodologies for classical species sampling problems, generalized species sampling problems emerging in biological and physical sciences, and question thereof in the context of optimal design of species inventories; ii) the use of recent mathematical tools from the theory of differential privacy to study the fundamental tradeoff between privacy protection of information, which requires to release partial data, and Bayesian learning in species sampling problems, which requires accurate data to make inference.

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