Project Description:

• The selected intern will develop and implement GIS and AI techniques to analyse and model historical coastal changes, working with a variety of digitised historical and modern mapping data. Key activities include:

• Data Collection & Preprocessing: Assemble and preprocess historical and contemporary coastal geographic data.

• GIS Analysis: Employ Geographic Information Systems to analyse spatial data, identify patterns, and visualise coastal landscape alterations.

• AI Modelling: Apply AI methodologies for predicting and validating future coastal changes.

• Results Interpretation & Reporting: Interpret results, providing meaningful insights into coastal change trends for reports and presentations.

• The intern will engage in periodic meetings with advisors from the University of Southampton's Geography Department, benefiting from their expertise and guidance. This collaborative project offers a unique opportunity to make a significant contribution to environmental research.

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Applicant Specification:

• Academic:

• Enrolled in or a recent graduate of a degree in Geography, Environmental Science, Computer Science, or related fields.

• Minimum Requirements:

• Strong analytical skills and attention to detail.

• Basic proficiency in GIS with an interest in AI and machine learning.

• Self-motivated with a keenness to learn and contribute to environmental research.

Desirable Requirements:

• Experience with coastal geography or environmental studies.

• Familiarity with machine learning tools and techniques.

• Excellent communication skills and ability to work collaboratively in a team, including with external advisors.

The inclusion of collaboration with the University of Southampton's Geography Department emphasises the project's commitment to academic rigour and provides additional opportunities for learning and networking. The project will equip the intern with a robust skill set, preparing them for a future in environmental research and technology.

Process details:

• 8 weeks minimum fixed term contract to be agreed with successful candidate

• In-person Induction Event to be held on Monday 24 June 2024, and an in-person/hybrid Showcase Event to be held in the Autumn of 2024

• 5 days holiday to be taken during the placement