Location: Memorial University, St. John’s, Newfoundland and Labrador, Canada
Position type: Funded, full-time graduate research opportunity
Start date: As soon as admission to Memorial University is secured
Work arrangement: On campus in St. John’s; this is not a remote position
Position overview
Applications are invited from highly motivated students for funded M.Eng. and Ph.D. graduate research opportunities at Memorial University.
The successful applicants will conduct advanced research at the intersection of GeoAI, multimodal geospatial foundation models, agentic and explainable artificial intelligence, and remote sensing for maritime intelligence.
The research includes collaboration with C-CORE (http://c-core.ca) and opportunities to address real-world maritime challenges using advanced AI methods, synthetic aperture radar (SAR), Earth-observation imagery, and multimodal data.
Specific thesis topics will be developed according to the applicant’s background and may include:
- Multimodal geospatial foundation models
- Foundation-model adaptation for remote sensing
- Agentic AI for Earth observation and maritime intelligence
- Explainable and trustworthy AI
- SAR image understanding and interpretation
- Multimodal data fusion
- Maritime surveillance and domain awareness
- Computer vision and deep learning for Earth-observation data
Responsibilities
Successful applicants will be expected to:
- Develop and evaluate machine-learning and deep-learning methods for geospatial and remote-sensing applications
- Work with SAR, optical, geospatial, and other multimodal data
- Implement models and experiments using Python and frameworks such as PyTorch or TensorFlow
- Conduct literature reviews and formulate original research questions
- Analyze results rigorously and document reproducible experiments
- Prepare technical reports, research papers, presentations, and a graduate thesis
- Collaborate with academic supervisors, research partners, and C-CORE personnel
Required qualifications
Applicants must have:
- A degree in electrical or computer engineering, computer science, geomatics, remote sensing, applied mathematics, or a closely related discipline
- A strong academic record; competitive applicants will normally have an average of at least 80% or equivalent in their most recent degree
- Experience with Python programming
- A solid foundation in machine learning, deep learning, computer vision, signal processing, remote sensing, or a closely related area
- Strong analytical, problem-solving, and technical communication skills
- The ability to conduct research independently while contributing effectively to a collaborative team
- The ability to satisfy Memorial University’s applicable graduate admission and English-language proficiency requirements
Additional expectations by degree
M.Eng. applicants should hold, or be close to completing, a relevant bachelor’s degree and provide evidence of strong technical preparation through research, a capstone project, publications, software, or other relevant work.
Ph.D. applicants should normally hold, or be close to completing, a relevant research-based master’s degree. Evidence of independent research ability is required. Publications, a strong thesis, or substantial research experience in a relevant field will be considered an asset.
Preferred experience
Experience in one or more of the following areas is highly desirable:
- Foundation models, vision-language models, or multimodal learning
- Remote sensing or geospatial artificial intelligence
- Synthetic aperture radar or radar signal processing
- Computer vision and image processing
- Large-scale model training, fine-tuning, or parameter-efficient adaptation
- Explainable, trustworthy, or uncertainty-aware AI
- Agentic AI or tool-using AI systems
- Git, Linux, high-performance computing, or cloud-based development
- Research publications, open-source software, or relevant technical projects
Applicants are not expected to have experience in every listed area. However, applications should demonstrate strong preparation in at least one relevant technical area and explain clearly how it relates to the proposed research.
Funding
Graduate research funding is available for selected applicants. The funding package, duration, and applicable terms will be communicated to shortlisted candidates and confirmed in writing as part of the admission and funding process.
How to apply
Email one combined PDF to:
- [contact details available in the full listing]
- [contact details available in the full listing]
Use the following email subject:
Graduate Application – GeoAI – M.Eng. or Ph.D. – [Your Full Name]
The PDF must contain, in this order:
- Academic CV
- Transcripts for all completed and current degrees
- A research statement of no more than two pages explaining:
- The degree sought: M.Eng. or Ph.D.
- Relevant research interests and experience
- Alignment with this opportunity
- One or two potential research questions of interest
- Evidence of relevant work, such as publications, thesis work, technical reports, GitHub repositories, software projects, or a project portfolio
In the application email, please also state:
- Degree sought
- Current or most recent degree
- Overall average or GPA and its grading scale
- Expected availability to begin the program
Applications that do not include transcripts, academic results, and concrete evidence of relevant technical work may not be reviewed. Generic applications that do not explain the applicant’s alignment with the research will not be prioritized.
Applications will be reviewed on a rolling basis until suitable candidates are identified. Only shortlisted applicants will be contacted.
Admission is subject to Memorial University’s formal graduate admissions process and requirements.
Dr. Khalid El-Darymli participates solely in his academic capacity as an Adjunct Professor at Memorial University.
