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Company Description: UNIVERSIDAD de TARAPACÁ

Job Description: The Universidad de Tarapacá is seeking a POSTDOCTORAL RESEARCHER FOR THE ANILLO PROJECT ATE250021 “Resilient Agro-Food Systems: Technological and Heritage-Based Integration for Sustainable Development in the Atacama Desert Header”.


The Postdoctoral Researcher will lead high-level scientific research within the framework of the ATE250021 Project around work packages WP1 “Digital Twin and Precision Agriculture” and/or WP2 “Climate Dynamics and Meteorological Integration”. The primary goal is to develop advanced modeling framework for precision agriculture considering extremes weather conditions and projected weather changes.


Main Functions


  • The selected Postdoctoral Researcher will play a central role in the design, development, and implementation of AI-powered Digital Twin technologies and climate-informed agricultural systems for hyper-arid environments.
  • Integrate climate projection models into the Digital Twin platform.
  • Collaborate in the design and execution of field experiments and pilot implementations.
  • Work collaboratively with multidisciplinary teams including engineers, agronomists, and climate scientists.
  • Publish research findings in high-impact peer-reviewed journals. Contribute to project reports and deliverables. Support the preparation of new research proposals and funding applications.


REQUESTED COURSE OR SPECIALIZATIONS


  • PhD in Engineering, Applied Mathematics, Environmental Sciences, Agricultural Engineering, Computer Science, or related fields.
  • Proven specialization in at least one of the following: Digital Twin, Computational Fluid Dynamics, Numerical Modeling, Downscale Global Climate Models, Machine Learning, PINNs.


Work Experience

PhD Degree


TECHNICAL COMPETENCIES OF THE POSITION

  • Expertise in simulation environments such as MATLAB/Simulink, ANSYS, OpenFOAM, programming languages like Python Fortran or C++.
  • Practical experience with Geographical Information Systems (GIS) and Remote Sensing.
  • Knowledge of Internet of Things, Data Sciences, machine Learning algorithms, Computational Fluid Dynamics.
  • Advanced English proficiency for technical writing and international academic dissemination.
  • Ability to work independently and manage research tasks.


JOB CONDITIONS

  • Contract Type: Fixed-term, 24 months.
  • Salary: CLP 2,147,250 gross per month (taxes included).
  • Work Schedule: Monday to Friday (full-time).
  • Work Modality: Hybrid, with periodic visits to Arica, Saucache.
  • Application deadline: May 15, 2026.


Application Procedure

Applications must be submitted as PDF file to Cristóbal Castro (ccastro@academicos.uta.cl), with a copy to Camilo Riveros (criverosb@academicos.uta.cl), using the subject line: PostDoc_WP1_WP2_ATE250021


The application must include the following documents:

  • Updated Curriculum Vitae, including a list of publications, research projects, teaching experience and outreach activities.
  • Copies of academic degrees and certificates.
  • Statement of purpose and work plan (maximum 2 pages).
  • Names and email addresses of two academic referees (national and/or international).


Project Background

V.I.I. Problem Definition. Chile’s hyper-arid zones—particularly the Atacama Desert —represent one of the most agriculturally extreme and scientifically strategic environments on Earth. With annual precipitation below 2 mm in certain areas, high solar irradiance, saline-boric soils, and severe groundwater limitations, the region exemplifies the multiple and intersecting stressors that global agri-food systems are expected to face under intensifying climate change scenarios. Nevertheless, smallholder and Indigenous communities have sustained cultivation in this territory for centuries, using ancestral knowledge, drought-resilient germplasm, and adaptive water management systems such as gravity-fed irrigation and terracing. These agroecological strategies reflect a unique form of resilience that remains underrecognized in conventional agricultural innovation frameworks.

This project proposes an integrative and transdisciplinary framework for climate-resilient agriculture that bridges cutting-edge digital technologies with local agroecological heritage. Rather than viewing innovation and tradition as opposing forces, it promotes a hybrid paradigm that combines AI-powered Digital Twin systems, localized climate intelligence, and ancestral agroecological knowledge to create adaptive, sustainable, and territorially grounded agro-food systems.


The initiative is organized into three interdependent Work Packages (WPs), each addressing a critical dimension of resilience:


1. WP1- Digital Twin and Precision Agriculture, centers on the design, development, and calibration of AI powered digital twin systems tailored to greenhouse and field agriculture. These systems leverage real-time environmental data gathered through IoT sensors and integrate it with artificial intelligence and physics-based simulations to optimize key variables such as air temperature, relative humidity, solar radiation, water requirements, and energy use. Unlike conventional digital agriculture tools, our systems will be co-designed for data-scarce and environmentally extreme conditions, offering robust models for low-input, high-risk contexts.


2. WP2- Climate Dynamics and Meteorological Integration, focuses on producing downscaled, spatially explicit climate projections for 2050, 2080, and 2100 using global climate models and Shared Socioeconomic Pathways (SSPs). These outputs include high-resolution land suitability maps for crops, fog harvesting, and solar-powered systems, fully integrated into the Digital Twin platform. This anticipatory climate intelligence will support adaptive management and territorial planning under future scenarios.


3. WP3- Germplasm Conservation and Cultural Agro-Heritage, is dedicated to the documentation, preservation, and integration of ancestral agricultural practices and local crop varieties adapted to extreme aridity. Through ethnoagricultural research, participatory workshops, and field trials, traditional practices—such as seed exchanges, terraced farming, and crop rotation—will be documented and georeferenced. At the same time, native crops such as the Poncho Negro tomato, Alta Sierra alfalfa and lluteno maize will be evaluated for their genetic and agronomic resilience under abiotic stress. These findings will inform both genetic improvement programs and the integration of agroecological principles into digital agriculture frameworks. A sui generis IP strategy will protect Indigenous rights over genetic resources and traditional knowledge, reinforcing ethical innovation.


The project’s methodology is inherently transdisciplinary, involving collaboration across agricultural sciences, climate modeling, engineering, computer science, archaeology, and public policy. It is anchored in sustained engagement with local communities and Indigenous organizations, ensuring cultural relevance, social ownership, and territorial legitimacy of results. Co-creation will be operationalized through FPIC-based protocols, territorial workshops, and participatory validation mechanisms. Ultimately, this initiative seeks to position Chile as a global benchmark for climate-resilient, inclusive, and low-carbon agricultural innovation in extreme environments, with broad implications for food security, environmental governance, and sustainable development.

 

 

General Objective: To generate transformative scientific knowledge and implement integrative technological and cultural strategies to strengthen climate-resilient agro-food systems in Chile’s hyper-arid zones—particularly the Atacama Desert Header —by combining AI-powered Digital Twin platforms, localized climate change modeling, and the conservation and valorization of ancestral agroecological knowledge, while producing policy-relevant outputs with national and global impact to inform inclusive, sustainability-oriented public policy frameworks.

 

Specific Objectives:

 

S.O.1. Design and implement an AI-powered Digital Twin system to enhance Agricultural Adaptability and Resilience in hyper-arid environments. Develop, calibrate, and validate a real-time simulation platform that integrates IoT sensors, artificial intelligence, and subsystem models (thermal, hydric, radiation, and plant physiology) to optimize environmental control, crop yield, and resource efficiency in controlled agricultural settings.

 

S.O.2. Generate localized climate change projection models and integrate them into predictive agrotechnological systems. Analyze climatic data specific to Chile’s hyper-arid zones; Develop high-resolution, spatially explicit climate change projections to support anticipatory decision-making in agricultural planning and climate risk management; and Embed these models within the Digital Twin framework to consider future climate trends in the technology development.

 

S.O.3. Conserve and integrate local germplasm and ancestral agroecological knowledge for climate-resilient agriculture. Document and evaluate traditional farming techniques, germplasm diversity, and agro-food heritage—such as native crop varieties, seed exchange systems, and knowledge embedded in cultural practices— through participatory research, ethnohistoric and advanced genetics. Integrate these insights into modern agrotechnological systems to ensure biodiversity conservation, reinforce food sovereignty, and optimize adaptation to water scarcity and soil stress, and support the development of inclusive public policies and market strategies rooted in local knowledge.