Doctoral Candidate Spatial statistics for integrating IoT field sensor data

• A written example of your scientific work, such as an MSc thesis, a recent individual report, or an article
• First-round (online) interviews are scheduled in the second half of November. A (possible) second-round interview might take place in early December.
• Applications that do not include the above-mentioned information will not be considered.

Screening is part of the selection procedure.

For questions about working and living in the Netherlands, please consult the official website of the Dutch Government or the Expat Center East Netherlands website.

• An MSc related to geoinformation science and earth observation, spatial and spatiotemporal modelling, spatial statistics, and/or machine learning. You enjoy applying these modelling skills to analyse and explain interactions within environmental and ecological systems
• Able to handle and integrate multi-temporal and multi-spatial data from multiple sources, including earth observation data, IoT-based sensor data, and in situ data
• Comfortable with programming or scripting in Python or R for data analysis and for bringing your models to the data
• Good communication skills and an excellent command of the English language
• Enjoy engaging in team science with project and societal partners
• Willing to travel and to collect data in the field; familiarity with cocoa farming in Ghana is an advantage given the required interaction with local managers.
• Motivated to contribute to knowledge dissemination and uptake activities for societal partners while also embracing Open Science and FAIR data principles
• A passion for education in an international classroom.
• Interested in developing educational materials and teaching topics related to spatial modelling and understanding interactions in environmental systems.
• A dedicated and considerate advisor for MSc students.

• An inspiring multidisciplinary, international and academic environment. The university offers a dynamic ecosystem with enthusiastic colleagues in which internationalization is an important part of the strategic agenda
• Full-time position for four years with a qualifier after 6 - 9 months
• A professional and personal development programme within Twente Graduate School
• Gross monthly salary of € 3,204.00 in the first year, which increases to € 4,051.00 in the fourth year
• A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%
• Excellent support for research and facilities for professional and personal development
• A solid pension scheme
• A total of 41 holiday days per year in the case of full-time employment
• Excellent working conditions, an exciting scientific environment, and a green and lively campus.

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.