진학프로석·박사 채용 접수 플랫폼

AgrifoodTECH COFUND

Research Code: C1-POSTHARVEST MANAGEMENT

We are seeking innovative research proposals that aim to contribute to advances in postharvest management of fresh produce through open and data-driven approaches, supporting improved understanding of quality evolution and food losses across supply chains.

Supervisor: Antonio Javier García Sánchez.

Co-supervisor: Encarna Aguayo Giménez.

Keep in mind that supervisors are not allowed to get involved in the project proposal preparation.

Context: Food losses and quality degradation in fresh fruits and vegetables represent a major challenge across postharvest supply chains, where products remain biologically active and highly sensitive to handling and storage conditions. Variations in environmental factors such as temperature, humidity and gas composition strongly influence physiological processes and deterioration dynamics, contributing to significant losses during processing, transport, retail and storage stages.

In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have led to a substantial growth in the volume and diversity of data available in postharvest and agri-food systems. While these developments have enabled significant advances in other sectors, important scientific questions remain regarding how heterogeneous postharvest data can be effectively interpreted and linked to biological processes in fresh produce, and how such information can contribute to a deeper understanding of quality evolution and loss mechanisms within complex supply chains.

The problem to address: The growing availability of sensor data, digital information and analytical capabilities in postharvest systems has expanded the potential to observe conditions across fresh produce supply chains. However, important scientific challenges remain in understanding how heterogeneous data can be meaningfully linked to the biological processes governing quality evolution in perishable products.

Key open questions relate to the relationships between measured environmental conditions, physiological responses of fresh produce, and the resulting dynamics of quality degradation and losses under real postharvest conditions. These knowledge gaps limit a critical assessment of the potential role of data-driven approaches in postharvest management and highlight the need for further fundamental and exploratory research at the interface between postharvest science and data-oriented analysis.

Objectives:

• • Advance understanding of how digital sensing and monitoring approaches can capture environmental and physiological information relevant to quality evolution in fresh produce supply chains.
• • Explore approaches for integrating and interpreting data generated within digitally enabled postharvest systems, including distributed and near-real-time data streams.
• • Investigate the potential role of data-driven and artificial intelligence-based modelling approaches in representing product quality and shelf-life dynamics under variable handling conditions.
• • Examine how insights derived from digitally enabled and data-oriented analyses may contribute to scientific understanding of postharvest management in selected contexts.

Expected Outcomes: Research conducted under this line may contribute to advancing understanding of digitally enabled and data-driven approaches within postharvest management of fresh produce supply chains. Possible outcomes may include:

• • Improved understanding of how information generated through digital sensing and monitoring relates to environmental conditions, physiological responses and quality evolution in fresh produce.
• • Conceptual insights into the potential role and limitations of artificial intelligence and data-driven modelling approaches in representing quality degradation and shelf-life dynamics.
• • Exploratory perspectives on how digital technologies and data-oriented analyses may inform scientific discussions on food losses and traceability in postharvest systems.
• • Broader scientific perspectives on the role of digitalization and computational approaches in advancing postharvest research and management of fresh produce supply chains.

Candidate Qualifications (if any):

Candidates may come from a broad range of disciplines relevant to postharvest systems and data-oriented research, including postharvest biology, food technology, data science, computer science, telecommunication engineering, or other related scientific or engineering fields. Experience or familiarity with research areas such as postharvest quality, food losses, digitalization in agri-food systems, data analysis or modelling approaches may be considered an asset.

Who can apply?

Candidates must have a PhD degree and must have obtained their (first) PhD a maximum of 8 years before the deadline of each call according to the MSCA scheme. Candidates must also comply with the MSCA mobility rule, must have to identify a main supervisor and are expected to define their Individual Research Proposal (IRP).

In support of gender dimension and other diversity aspects, UPCT is committed to ensuring full transparency in the recruitment, evaluation and selection process, in which all candidates are evaluated equally on the basis of their achievements and are not discriminated against on the basis of race, ethnicity, sexual orientation, age or socio-economic background. There is also a Code of Ethics which sets out the main moral aspirations to be respected and complied with by UPCT members; good practices to be carried out for the equal treatment of people regardless of their condition or circumstance; and a a service for the support of candidates with some kind of disability (whether intellectual, physical, sensory, mental or other illnesses) during the application process and throughout the IRP.

Elegibility criteria: here.