Research Associate

Job Summary

The AI-Enhanced Biogeochemical Modelling and Digital Ocean Science Services project will establish a Virtual Ocean Lab (VOL), a digital platform for hybrid physics-AI biogeochemical (BGC) modelling in the Northwest Atlantic. The work addresses persistent bias and parameter uncertainty in process-based BGC models for chlorophyll, nutrients, primary productivity, and oxygen. Reporting to the Principal Investigator, the Research Associate will support scientific and technical delivery of the pilot, with emphasis on hybrid physics-AI methods for BGC parameterization, bias correction, diagnostics, and reproducible workflow development for a Northwest Atlantic shelf testbed.

Key Responsibilities

• Develop and evaluate deep learning or related AI methods that adjust BGC productivity, physiological parameters, and ecological terms within a process-based BGC framework while maintaining physical consistency.
• Build reproducible workflows that integrate BGC model output, satellite chlorophyll, BGC-Argo, and oxygen observations for training, testing, and evaluation.
• Conduct experiments comparing baseline and hybrid BGC simulations against satellite and in situ observations using standard evaluation metrics. Complete AI-enabled geophysical data analysis.
• Develop algorithms, Virtual Ocean Lab (VOL) notebooks and related diagnostics to visualize bias reduction, profiles, time series, and other model performance indicators.
• Prepare analysis-ready products, technical documentation, figures, reports, manuscripts, and presentations for project reporting and scientific communication.
• Maintain organized records of workflows, code, model configurations, and results, and participate in project meetings, milestone tracking, and collaboration across the Department of Fisheries and Oceans and Dalhousie teams.

Note

The successful applicant may work in-person on campus, primarily remotely (from within Nova Scotia with occasional presence in-person on-campus), or perform a hybrid of the two as agreed by all parties based on operational requirements.

Required Qualifications

• PhD in Oceanography, Environmental Science, Applied Mathematics, Computer Science, or a closely related field plus approximately three years’ relevant experience in ocean biogeochemistry and/or physical oceanography, including independent research leadership, experience in complex AI-physics integration, and peer-reviewed outputs (or equivalent combination of training and experience).
• Expertise in artificial intelligence for ocean applications, including deep learning, physics-informed or physics-guided models, and/or transfer learning applied to marine BGC variables or related fields.
• Demonstrated ability to process, analyze, and interpret oceanographic datasets such as BGC-Argo float profiles, satellite ocean colour/chlorophyll, and/or model output from systems such as NEMO or CanOE.
• Proven advanced scientific programming skills in Python and/or MATLAB, with experience with PyTorch and/or TensorFlow and workflow tools for data handling and model training.
• Demonstrated scholarly productivity, peer-reviewed publications and presentations in marine biogeochemistry, ocean AI, remote sensing, or marine environmental analysis.
• Proven ability to work independently and collaboratively with strong analytical and communication (verbal and written) skills, scientific judgment, and attention to detail.

Assets

• Experience with hybrid physics-AI approaches, physics-informed neural networks, or AI-augmented parameter estimation in ocean or climate modelling.
• Familiarity with BGC-Argo, CMIP6, and satellite ocean colour products such as MODIS, including reconstruction of subsurface or three-dimensional biogeochemical fields.
• Experience in collaborative, multi-institutional projects involving ocean modelling, satellite remote sensing, and digital science infrastructure.
• Exposure to hybrid AI frameworks from related marine applications.

Job Competencies

The successful candidate is expected to demonstrate proficiency in Dalhousie’s and competencies, in particular:

- Self-Awareness & Professionalism
- Integrity
- Adaptability
- Knowledge & Thinking Skills
- Communication

Additional Information

Dalhousie University supports a healthy and balanced lifestyle. Our total compensation package includes a voluntary RRSP, health and dental plans, a wellness allowance, an employee and family assistance program and a tuition assistance program.

Application Consideration

Non-probationary bargaining unit employees (PSAC Local 86001) are given priority consideration. All other applications will be considered when there is no qualified candidate from the bargaining unit.

Full-time students currently enrolled at an educational institution are not eligible for regular Grant-Paid positions.

We sincerely appreciate all applications and note that only candidates selected for an interview will be contacted directly by the hiring department or People and Culture.

Diversity Statement

Dalhousie University commits to achieving inclusive excellence through continually championing equity, diversity, inclusion, and accessibility. The university encourages applications from Indigenous Peoples of Turtle Island (especially Mi’kmaq), persons of Black/African descent (especially African Nova Scotians), and members of other racialized groups, persons with disabilities, women, persons identifying as members of 2SLGBTQIA+ communities, and all candidates who would contribute to the diversity of our community. In accordance with our Employment Equity Policy, preference will be given in hiring processes to candidates who self-identify as members of one or more of the equity-deserving groups listed above.