Jobid: 638356939956753658 (0.0503)
The University of Twente, Faculty ITC, wishes to increase the number of women in the faculty to have a more balanced staff profile. During all phases of the selection process, we will therefore prioritize selecting women who fit the profile.
Your challenge
The Dutch government, through the Ministry of Education, Culture and Science, has responded to the current global environmental challenges by establishing sector plan positions in critical scientific domains. At the Department of Environmental Resources, one of our activities is to address these challenges by developing and applying Geostatistical models for bridging knowledge gaps, data scarcity and uncertainty gaps, and governance gaps related to monitoring the environment on which humans depend.
A part of this is spatial statistics of sensor data integration for nature-inclusive solutions for monitoring stress and diseases of tree crops. Tree crops like cocoa, apart from their direct economic functions for smallholder farmers, sit at the intersection of many beneficial ecological functions (including carbon sequestration and cultural identity). However, these functions are threatened by environmental stressors and diseases such as Cocoa Swollen Shoot Virus (CSSV) disease, which depend on the complex web of interactions between and within above-ground and below-ground biotic and abiotic factors. The prevailing data and methodological gaps that have perpetuated knowledge gaps in the spatial and spatiotemporal patterns of tree disease, and the widened governance gaps of farms, have motivated this topic.
You will develop spatial statistical methods to integrate ground-based IoT sensor data, remote sensing data, and in-situ data for mapping the spatial trends of cocoa diseases. You will be involved in setting up an IoT sensor network in cocoa farms in Ghana. You are expected to address data integration challenges, including (1) spatial misalignment of networks, (2) temporal misalignments of observations, (2) probabilistic or likelihood misalignments, and (3) data quality issues, such as uncertainties in measurements, sparsity of network coverage resulting in small N, missing data resulting from malfunction of sensors, and outliers. For the purposes of evaluating model transferability, you will make a comparison with other economically important tree crops in food forests in the Netherlands. You will design a measurement setup for cocoa trees and review the wide range of applications of IoT sensors, their uncertainties, and the observable variables above and below ground that are important for predicting tree crop diseases and stresses. You will also explore simulation scenarios to evaluate the impact of indigenous and formal farming management practices on plant diseases.
Your profile
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