PhD: Self-Supervised Learning for Sparse Spatio-Temporal Data

University of East Anglia; Norwich; Presencial; GBP 21.000 - 22.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Three-year stipend

UK funded PhD stipend (home tuition)

Descripción de la vacante

The University of East Anglia’s School of Computing Sciences invites applications for a PhD project titled “Can AI Learn When Data Are Scarce?” under Professor Beatriz de la Iglesia. You will explore self-supervised learning with spatial and temporal data to develop novel algorithms and address real-world challenges.

Based on an ongoing NERC-funded project, the work aims to predict microbial contamination in bathing waters and bridges AI methods with environmental science and hydrodynamic

Formación

• Minimum 2:1 in Computer Sciences, Data Science or related subject.
• Interest in machine learning and research.
• Ability to publish methodological research.

Responsabilidades

• Develop self-supervised learning methods for scarce data.
• Create novel algorithms exploiting space, time and data sources.
• Publish research findings and contribute to open-source tools.

Conocimientos

Machine learning

Self-supervised learning

Python

Research writing

Educación

2:1 in Computer Sciences, Data Science or related subject

Descripción del empleo

The University of East Anglia’s School of Computing Sciences invites applications for a PhD project titled “Can AI Learn When Data Are Scarce?” under Professor Beatriz de la Iglesia. You will explore self-supervised learning with spatial and temporal data to develop novel algorithms and address real-world challenges.

Based on an ongoing NERC-funded project, the work aims to predict microbial contamination in bathing waters and bridges AI methods with environmental science and hydrodynamic