DL Research Assistant: Malaria Habitat Mapping

Aberystwyth University; Aberystwyth; Hybrid; GBP 28,000 - 34,000; Full time

Benefits offered by this job

Flexible working policy

Hybrid Working

36.5-hour week

Generous leave entitlements

Job summary

Aberystwyth University invites applications for a Research Assistant to support the Still Waters project, using hydrological models to improve malaria transmission estimates. The role focuses on training data creation, DL pipelines, and malaria habitat mapping from satellite imagery.

You will collaborate with Earth Observation group colleagues and hydrologists at Leeds University, contributing to publications and delivering rigorous validation of maps and methods.

Qualifications

• Experience in Python programming.
• Experience in using cloud-based services like Google Earth Engine and Google Colabs.
• MSc (or equivalent experience) in GIS and remote sensing.
• Working knowledge of GIS platforms, like QGIS.
• An understanding of different approaches to satellite image classification and validation.

Responsibilities

• Develop a comprehensive training data set (i.e. annotated satellite imagery) representing target malaria aquatic habitat types (notably, river corridors).
• Deploy the training data set within Deep Learning pipelines developed by the Earth Observation research group (organised as code blocks within a Python notebook).
• Evaluate the output maps both as an iterative process to determine changes in the Deep Learning pipeline, and as a formal validation of final maps.
• Deploy the final model to produce malaria habitat maps.
• Assist in writing an academic paper to publish the methods and findings of this work.
• Collation of existing datasets on malaria habitat location.
• Develop composites of satellite imagery (likely PlanetScope imagery).
• Manually digitise satellite imagery with known malaria habitats within a GIS environment.
• Modify Python notebook to read and process the training dataset and satellite image composites within an established Deep Learning pipeline.
• Monitor outputs, alongside project partners, to determine modifications of the pipeline to improve the model.
• Validate the final model and final output map.
• Collate details of the approach and findings to present to the wider project team.
• Contribute to a publication reporting the approach and findings.

Skills

Python programming

Google Earth Engine

Google Colabs

GIS

QGIS

Satellite image classification

Education

MSc in GIS and remote sensing

Tools

Google Earth Engine

Python notebook

Job description

Aberystwyth University invites applications for a Research Assistant to support the Still Waters project, using hydrological models to improve malaria transmission estimates. The role focuses on training data creation, DL pipelines, and malaria habitat mapping from satellite imagery.

You will collaborate with Earth Observation group colleagues and hydrologists at Leeds University, contributing to publications and delivering rigorous validation of maps and methods.