🛰️ Research Assistant Position in GeoData Science and Machine Learning

Research Area

Mobility is pivotal in shaping societal, economic, and environmental dynamics. Understanding movement patterns provides insights into how natural and human-made environments impact and influence transportation systems. In the maritime domain, mobility has been a cornerstone of global trade and cultural exchange, accounting for over 80% of the world’s trade volume.

The analysis of mobility data, such as that collected through the Automatic Identification System (AIS), enables the modeling of movement across origins and destinations, trajectory forecasting, and environmental impact assessment. By leveraging cutting-edge data science techniques, we aim to understand and improve decision-making in mobility systems, focusing on maritime traffic, which is highly influenced by atmospheric and oceanic conditions.

This position offers the opportunity to work on data preparation, model development, and visualization of maritime mobility patterns using AIS and hindcast environmental data. The successful candidate will help advance methodologies in mobility data modeling, enabling better insights into the interplay between human activities and environmental factors.

Position Details

This is a grant-funded, limited-term contract role supported by the Faculty of Computer Science at Dalhousie University. The Research Assistant will work under the supervision of Prof. Gabriel Spadon, contributing to the AISViz project. The position focuses on developing technical solutions for maritime mobility data mining and visualization.

The successful candidate will have opportunities to engage with an interdisciplinary team, collaborate with national and international researchers, and contribute to publishing project results.

Contract Type: Limited-term, grant-funded.
Location: Dalhousie University, Halifax, Nova Scotia, Canada.
Start Date: As soon as possible after hiring.

Qualifications

You are encouraged to apply even if you do not meet all the requirements of the ideal candidate.

The ideal candidate will be someone who:

• 📌 Holds a Master’s degree in a relevant field, preferably computer science or a closely related discipline.
• 📌 Has excellent academic records and good interpersonal skills.
• 📌 Is fluent in the English language (IELTS 7 or above, or equivalent).
• 📌 Has a strong interest in and ability to learn new research methods and skills.
• 📌 Has an interest in conducting interdisciplinary, applied research.
• 📌 Has experience in statistical analysis and strong programming skills (Python and Rust – preferred).
• 📌 Has experience programming on Deep Learning Frameworks (Pytorch and TensorFlow – preferred).
• 📌 Has strong time management, organizational, and project management skills.
• 📌 Works well both independently and as part of an interdisciplinary team.
• 📌 Can communicate effectively with researchers and non-academic members of governments, communities, and the private sector.

Application

Applicants should submit the following documents:

• 📄 A detailed curriculum vitae.
• 📜 A transcript of records from graduate and undergraduate programs.
• 📞 The contact information of two referees.

Send your application materials via email to Prof. Gabriel Spadon (spadon@dal.ca) with the subject line “Lastname Firstname CS-RA #2024-07” (e.g., “Doe John CS-RA #2024-07”). Applications will be reviewed starting July 1, 2024, and will close September 1, 2024. Only selected candidates will be contacted for an interview.