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Crea una Job Alert gratuita per ref. 4365_att. 4 - innovative techniques for measuring physicochemical parameters in agri-food matrices for product and process controlinnovative techniques for measuring physicochemical parameters in agrifood matrices for product and process control / italia

Ref. 4365_Att. 4 - Innovative techniques for measuring physicochemical parameters in agri-food matrices for product and process controlInnovative techniques for measuring physicochemical parameters in agrifood matrices for product and process control

Pubblicato il 11-08-2026 - Italian Ministry of Education, University and Research in Italia

Organisation/Company Alma Mater Studiorum - Universita' di Bologna Department DISTAL - DIPARTIMENTO DI SCIENZE E TECNOLOGIE AGRO-ALIMENTARI Research Field Agricultural sciences Other Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 27 Aug 2026 - 12:00 (UTC) Country Italy Type of Contract Temporary Job Status Other Offer Starting Date 1 Jan 1970 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The project primarily focuses on the development and application of innovative systems for the non-destructive determination of quality attributes in agri-food products, materials, and substrates. Research activities will centre on the study,



development, and validation of sensors and systems for data acquisition, reading, processing, and transfer, including IoT devices, with varying degrees of process applicability, ranging from at-line to in-line. The techniques are mainly electromagnetic, based on the interaction of materials with radiation ranging from radio frequencies to optical wavelengths. A substantial part of the research activities will involve data processing, primarily using multivariate statistical techniques, aimed at developing models to estimate and predict the parameters of interest characterizing the agri-food matrices.

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