Research Scientist in Deep Learning & Water Resources Tulane University’s ByWater Institute invites applications for a staff research scientist in the field of artificial intelligence and deep learning applied to water resources management to begin in 2024. The scholar will work with ByWater Institute Director Dr. John Sabo and interdisciplinary teams from across Tulane University in support of programs in Data-Driven and Computational Water Sustainability.  This position supports and leads projects to develop data-driven decision support tools that assist our public and private sector partners in scoping, strategizing and designing deployment of nature-based solutions in large river basins across the world.  

Essential duties

The successful candidate will lead the development and application of state-of-the-art deep learning and AI techniques to address complex water resources management challenges. Key responsibilities include:

1. Design and implement advanced multi-modal deep learning models, including but not limited to ConvLSTM, Graph Neural Networks (GNNs), and Transformers architectures, to tackle a wide range of water-related prediction tasks. These may include hydrological modeling, water quality forecasting, and environmental impact assessment.

2. Develop innovative approaches to integrate physics-based models with deep learning frameworks, leveraging differentiable modeling techniques that combine domain knowledge with data-driven insights.

3. Explore and apply reinforcement learning and multi-objective optimization techniques to solve complex decision-making problems in water resources management, such as optimal placement and operation of green and gray water infrastructure.

4. Advance the field of AI-driven environmental modeling by researching and implementing novel architectures, loss functions, and training methodologies tailored to the unique challenges of water systems.

5. Collaborate with domain experts to identify key environmental variables and processes, and incorporate them into AI models to improve prediction accuracy and interpretability.

6. Develop scalable and efficient AI solutions capable of handling large-scale, high-dimensional datasets typical in basin-wide water management scenarios.

7. Investigate and implement techniques for model interpretability and uncertainty quantification to enhance the reliability and trustworthiness of AI-driven decision support tools.

Minimum qualifications

Applicants must have a Ph.D. in Computer Science/Engineering, Industrial Engineering, Applied Mathematics, or a related discipline and have a background in optimization with strong, demonstrated interest in high-performance computing, computational simulation, and tools for machine learning applied to space-time problems. Familiarity with computational methods for differentiable AI is highly preferred. One or two research papers on related topics, as well as experience of implementation and experimentation with automated experiments with complex simulation models, are also strongly preferred.

Desired qualifications

The ideal candidate will possess a strong background in deep learning and AI, with the ability to apply these skills creatively to solve complex environmental challenges. They should be capable of bridging the gap between cutting-edge AI research and practical applications in water resources management, demonstrating flexibility in tackling a wide range of related problems beyond those specifically outlined.

Applicants must submit:

1. Cover letter explaining how prior experience and qualifications are appropriate to the job activities.
2. Statement of research accomplishments. Applicants should describe experience and goals related to the research, highlighting strengths of the applicant’s experience. Applicants are encouraged to demonstrate their dedication to solving sustainability problems through research and scholarship, capacity to work effectively in interdisciplinary teams, and excellent communication skills. Special emphasis will be placed on candidates who explain how their research would both benefit from and advance ongoing activities around public–private partnerships to solve sustainability problems, who strongly integrate the pursuit of knowledge about earth science and modelling with end users outside of the ivory tower.
3. Curriculum Vitae or resume.
4. Letters of recommendation. Provide the name, phone number, address, and e-mail address of three references.

Only electronic applications will be accepted.

URL: https://jobs.tulane.edu/position/IRC31159