[Infovis] PhD position in Deep Learning and Visual Analytics, University of Montpellier, France

[Arnaud Sallaberry]

Title. Explaining the trajectories of jellyfishs with heterogeneous text 
and images mining and visualization

Key words. Ecology, Deep learning, Explainability, Visual Analytics, 
Analysis of spatiotemporal series, Heterogeneous data visualization.

Context. The study of jellyfish outbreaks is important because they can 
have a negative impact on the provisioning of marine ecosystems services 
for human welfare. In addition, they can disrupt human activities, such 
as fishing, tourism and shipping. Jellyfish outbreaks are often linked 
to environmental changes such as water temperature, salinity and 
currents. Studying these factors can help predict future jellyfish 
outbreaks and develop management strategies to mitigate their impact on 
the environment and human activities. There are several methods to 
collect data on jellyfish, to better understand their distribution, 
abundance and behavior such as visual observations- as jellyfish can be 
visible on the surface of the water-, the use of plankton nets, 
satellite tags to track their movements, underwater cameras, etc. 
However, their capture and conservation and their fleeting and 
unpredictability appearances induce scarce scientific datasets.

Objectives. We here propose the collection and analysis of multilingual 
and heterogeneous documents to complement existing data (i.e., JeDI) 
with original data social media, press and scientific literature. Our 
objective is to depict patterns and trends in collected data, to map 
spacetime patterns of coastal aggregations and stranded jellyfish, and 
to track their diversity changes and the contribution of non-indigenous 
species to such phenomena (e.g. determination of invasion speed). The 
originality of our project is to disentangle the extent to which these 
events and the distribution patterns of non-indigenous species are 
related to climatic change and ecosystem degradation.

Methodology. We will follow a three steps strategy: 1) analysis of 
spatio-temporal series: multimodal supervised classification to exploit 
heterogeneous resources and spatio-temporal clustering to group similar 
data in space and time and to use species distribution modelling to 
understand their relation to the environment and model future evolutions 
2) heterogeneous data visualization: design, implementation and 
validation of an interactive visual interface for exploring the results 
of the first step (classes, patterns, clusters…) and the texts/images 
from the raw data. Particular attention will be paid to the 3) 
interpretability of the used methods.

Deadline for applications: 30 May 2023
Duration of the PhD: 3 years

Requirements:
- a master's degree in Computer Science with successful research experience
- advanced programming skills (design and implementation)
- a good academic level attesting to his/her ability to combine practice 
and theory
- a level of professional oral and written English
- general knowledge in the field of artificial intelligence
- an appetite for ecological issues

Procedure and contact
- Send 
tosandra.bringay at lirmm.fr,arnaud.sallaberry at lirmm.fr,maximilien.servajean at lirmm.fr 

- Your master's degree (if already obtained) and your transcripts
- Curriculum vitae with 2 references
- At least one letter of recommendation

- Any publications you may have

Applications are managed on a case-by-case basis. You will be informed 
promptly by email of the admissibility of your application and if you 
are invited to a first interview.

We look forward to your applications!
Sandra Bringay, Arnaud Sallaberry, Maximilien Servajean