Posted 6 days ago
Postdoctoral Researcher in Deep Learning for Groundwater Forecasting
@ European Geosciences Union (EGU)
Chicoutimi, Quebec
Overview
Université du Québec à Chicoutimi (UQAC) is hiring a postdoctoral researcher for a project with the Québec Ministry of the Environment (MELCCFP) on deep learning for groundwater-level forecasting. The role is full-time on site in Chicoutimi, up to 12 months on contract, at CAD 60,000 gross per year, with a PhD required.
Responsibilities:
- Develop deep learning models for groundwater-level forecasting
- Work on model generalization, uncertainty quantification and interpretability
- Conduct independent research in partnership with MELCCFP
Requirements summary:
- Completed PhD in AI, data science, computer science, applied mathematics or related field
- Demonstrated deep learning expertise, independently developing and debugging neural networks
- Excellent Python skills with PyTorch or TensorFlow
- Experience in time-series forecasting, transfer learning or uncertainty quantification is an asset
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-08
- First seen by our source
- 2026-11-14
- Source record updated
- 2026-11-14
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Mid level |
| Department | Research & science |
| Hours per week | 40 (estimated) |
| Travel | air: none · ground: none |
| Work setting | Office, Laboratory |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Education | Doctorate (required) |
| Fields of study | Artificial Intelligence, Data Science, Computer Science, Applied Mathematics |
Summary and details written by AI from the posting; the employer’s posting is the authority.
European Geosciences Union (EGU)
| Field | Value |
|---|---|
| Open jobs on Jobsy | 5 |
Position
Postdoctoral Researcher in Deep Learning for Groundwater Forecasting
Employer
Université du Québec à Chicoutimi (UQAC)
Public research university in Québec, Canada, conducting research in engineering, environmental sciences, and artificial intelligence.
Homepage: https://www.uqac.ca/
Location
Chicoutimi, Canada
Sector
Academic
Relevant divisions
Earth and Space Science Informatics (ESSI)
Hydrological Sciences (HS)
Type
Contract
Level
Experienced
Salary
CAD 60,000 gross per year (prorated if the appointment is shorter than 12 months)
Required education
PhD
Application deadline
Open until the position is filled
Posted
9 November 2026
Job Description
Postdoctoral Researcher – Deep Learning for Groundwater Forecasting
Université du Québec à Chicoutimi (UQAC), Canada
A postdoctoral position is available at UQAC for a research project conducted in partnership with the Québec Ministry of the Environment (MELCCFP).
The project focuses on developing deep learning methods for groundwater-level forecasting, with particular attention to model generalization, uncertainty quantification, and interpretability.
Required Qualifications
- Completed PhD in Artificial Intelligence, Data Science, Computer Science, Applied Mathematics, or a related field.
- Strong, demonstrated expertise in deep learning, including the independent development, training, evaluation, and debugging of neural network models.
- Excellent Python programming skills, with substantial hands-on experience in PyTorch or TensorFlow.
- Ability to conduct research independently, with strong analytical and problem-solving skills.
Applications without demonstrated practical expertise in deep learning and Python will not be considered.
Position Details
- Location: Chicoutimi, Québec, Canada (on-site).
- Salary: CAD 60,000 gross per year.
- Duration: Up to 12 months, with an expected start in early 2027.
- The funded research project ends in February 2028.
Please send your application to [email protected] with the subject "Postdoctoral Application – Deep Learning".
Your Application Must Include
- A CV with a list of relevant publications.
- A brief description of a deep learning project you personally implemented, specifying your technical contributions.
- Links to relevant code or other evidence of practical deep learning experience, if available.
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