🫵 Open PhD Positions in Computer Science at Dalhousie University

Jun 23, 2024·
Gabriel Spadon
Gabriel Spadon
· 4 min read
from Mayank Anand
Table of Contents

Research Area

Mobility is crucial for human life as it affects various aspects of our biology, society, economy, and culture. Human movement has historically influenced societies through population and environmental changes, and it continues to be a natural occurrence despite modern limitations. Similarly, maritime transportation has been fundamental to global trade since the Age of Exploration. It remains one of the most important methods of coordinating and moving goods, accounting for over 80% of the world’s trade volume today. The ability to move efficiently across vast distances by sea has not only shaped economic landscapes but also fostered cultural exchange and technological advancements, underscoring the significance of all forms of mobility.

Mobility data refers to sequences of positional data that change over time, showing the movement of people, objects, or animals from one place to another. This data includes different modes of transportation like cars, trains, buses, or planes and various types of vehicles within the same mode, such as fishing vessels, cargo ships, tankers, and leisure boats. Research in this field focuses on modeling movement between different origins and destinations through techniques like link prediction or label propagation and reconstructing detailed mobility paths or trajectories using time-series forecasting or clustering.

Studying mobility patterns and predicting movements can help us understand how trajectories are influenced by environmental factors and vice versa. For example, vessel mobility is affected by oceanic and weather dynamics, which directly impact the marine environment. Similarly, urban mobility patterns are interconnected with infrastructure developments and traffic regulations. Additionally, road conditions and congestion significantly impact vehicle movements, affecting fuel consumption and CO2 emissions. Pedestrian mobility is directly influenced by sidewalk layouts, which play a vital role in safety and walkability. Furthermore, air traffic mobility is connected to airport logistics, airspace management, and weather conditions, impacting overall transportation efficiency and resulting in flight delays.

The doctoral student specializing in this topic will contribute to the development of techniques (i.e., algorithms for data preparation, techniques for data modeling, and complete pipelines for knowledge extraction) for mobility data mining and modeling focusing on the ocean environment and using oceanic sensors, such as Automatic Identification System (AIS) and hindcast data describing weather and oceanic conditions to improve our capacity of modeling anthropogenic effects in the ocean while applying that knowledge to increase ocean awareness and enhance decision-making for ocean management.

Position Details

The Faculty of Computer Science is looking for a doctoral (PhD) student specializing in Ocean Mobility Data Mining within the Artificial Intelligence and Machine Learning cluster. The successful candidate will join a dynamic and dedicated research cluster and my research group, which is focused in data analytics, graph modeling/mining, and machine learning for spatiotemporal data.

The PhD student will receive full funding and will be enrolled in the Computer Science program at Dalhousie University in Halifax, Nova Scotia, Canada. The recruitment, admission, and processes will follow policies of Dalhousie University’s Faculty of Graduate Studies.

The PhD student will have the opportunity to learn and work in a research-intensive environment. They will also have access to advanced training and be able to develop their professional network. The university encourages participation in academic career development programs. Additionally, the student may have opportunities to conduct research visits national and international collaborators, as well as participate in conferences and workshops.

Qualifications

The ideal candidate will be someone who:

  • 📌 Holds a M.Sc. 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
  • 📌 Is able to communicate effectively with researchers and with non-academic members of governments, communities, and the private sector.

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

Application

Applicants should submit a detailed curriculum vitae, a transcript of records, and the contact information of two referees by email to me (spadon@dal.ca) with “Lastname Firstname CS PhD application #2025-01” as the subject line (i.e. put your last and first name in the email heading). Applications will be considered from July 1, 2024, until the position is filled. Note that only selected candidates will be contacted for an interview.