Professor of Practice, Department of River-Coastal Science and Engineering
Tulane University: Humanities & Sciences: School of Science and Engineering: River-Coastal Science and Engineering
The Department of River Coastal Science and Engineering (RCSE) at Tulane University invites applications for a Professor of Practice position to begin on May 1, 2025. Successful candidates will have a PhD in Civil or Environmental Engineering with a focus on Water Resources. Experience in teaching fluid mechanics, hydrology and hydraulic engineering is beneficial. This is a 12-month appointment that includes teaching in the summer term, as well as during the academic year. We welcome and encourage applications from members of underrepresented groups. Our friendly and collaborative department currently has 6 tenure-track/tenured faculty with a commitment to grow over the next few years. In our research and teaching, we take pride in our focus on rivers and coastal science and engineering and their interdisciplinary applications. More information about the department, its faculty team, and its undergraduate and graduate programs can be found at https://sse.tulane.edu/river. Review of applications will begin on November 1, 2024, and continue until the position is filled. The intent is for the position to be filled by May 1, 2025, in preparation for beginning instruction in the Summer 2025 term.
Tulane Professors of Practice in the School of Science & Engineering are faculty who design, enhance, and teach courses primarily at the undergraduate level, with the flexibility to instruct graduate courses as well as determined by the department’s need, while also contributing service to the departmental and university. Many of RCSE’s courses for advanced undergraduates are also available to graduate students. Professors of Practice hold term appointments that are renewable every three years (initially), and every five years after promotion to Senior Professor of Practice. The typical teaching load is three sections per semester (two preps). Our core courses focus on the fundamentals of water resources engineering and present course offerings are listed at https://catalog.tulane.edu/science-engineering/river-coastal-science-en….
Institutional and departmental support is available, for example, through Tulane’s Center for Engaged Learning and Teaching, and with teaching and course assistants. Tulane recognizes and rewards innovative and quality teaching. Candidates interested in creating and adopting pedagogical innovations, conducting scholarly activity in river coastal science and engineering education, or developing original elective courses will find many opportunities and support to pursue their interests. The successful candidate will have a record of excellent teaching and mentoring at the undergraduate level, and commitment to student-centered teaching and to increasing diversity in the classroom.
The selected candidate will participate in an interdisciplinary dual-degree program with the School of Architecture. This joint program offers a MS degree from RCSE and MLA from TuSA (MLA-MS RCSE (https://landscape-engineering.tulane.edu).
Tulane University is a private Carnegie-R1 university and a member of the prestigious Association of American Universities (AAU). Tulane brings together bold and creative scholars, scientists and students who are committed to crossing boundaries. Data-intensive discovery lies at the heart of research and education at Tulane and is fueled by Tulane’s new Connolly Alexander Institute for Data Science. Tulane resides in historic New Orleans, Louisiana – a city that takes the utmost pride in its vibrant music scene, world-class restaurants, rich confluence of cultures and traditions, and festive Mardi Gras parades. "The things that make life worth living – eating, drinking and the making of merriment – are the air that New Orleans breathes." - Adam Karlin, Lonely Planet. The Princeton Review ranks Tulane at or near the top for Most Engaged in Community (#1), College City (#1), Happiest Students (#4), Best-Run College (#4), and Quality of Life (#9).
Qualifications
PhD in Civil and Environmental Engineering with a focus on hydraulics, hydrology, and water resources. Prior teaching experience and proficiency with GIS, Professional Engineering License (or willingness/eligibility to obtain the PE license) are preferred.
Application Instructions
Review of applications will begin 11/01/2024 and will continue until the position is filled.
A complete application should include a CV, a teaching statement including teaching evaluations, a diversity statement, and at least three letters of recommendation that address teaching.
https://apply.interfolio.com/156397
Research Scientist in Deep Learning and 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
Research Scientist in Computational Optimization
Tulane University’s ByWater Institute invites applications for a staff research scientist in the field of computational optimization (e.g., simulation optimization, engineering design optimization, or applications of machine learning to high-dimensional or computationally costly black-box optimization) 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 corporate stewardship projects in large river basins across the world.
Essential duties
The successful candidate will develop efficient approaches for supporting the optimal siting and operation of green and gray water resources (e.g., wetlands, dams, and reservoirs) that best tradeoff among multiple design objectives engaging multiple stakeholders within large river basins. Ideally, green and gray water resources will be designed to simultaneously minimize risks for both droughts and floods while also meeting water demands under normal conditions, but water is a finite resource, and choices made in one part of a river basin are necessarily coupled to outcomes in regions far from that area. A practical matter complicating this multi-objective problem is that predicting these water flows is complex and can require different computational models for different aspects of the problem (e.g., predicting surface or ground water flows under different hypothetical driving functions), each of which may be very high dimensional due to the large area and the potentially very large number of options for how to intervene effectively. Furthermore, design choices should respect pragmatic sociopolitical constraints while also attempting to minimize the costs of implementation. Consequently, it is not possible to collapse all objectives into a single index as the relative weights of each objective may be unknowable, and so optimization approaches must use Pareto-optimal frameworks that result in sets of solutions for human decision makers to select from based on subjective criteria outside of the main optimization problem. The candidate will be able to formulate such multi-objective design problems and then use (and, as necessary, develop) simulation-based optimization methods that produce Pareto-efficient sets of watershed designs that inform and complement human decision making. Work will nominally involve: (a) automating the multi-objective evaluation of candidate watershed designs using of existing physics-based computational models for infiltration and flow, (b) developing methods for efficiently encoding the very high dimensional space of possible watershed designs to be amenable to computational optimization, (c) use and development of optimization metaheuristics and tools from simulation optimization to produce Pareto efficient watershed solutions, and (d) development and evaluation of surrogate models to massively decrease the time required for generating a candidate Pareto-efficient watershed design set.
Minimum qualifications
Applicants must have a Ph.D. in Computer Science/Engineering, Electrical 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 simulation optimization such as optimization metaheuristics. Familiarity with computational methods for multi-objective optimization 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
Experience with running large experiments in high-performance data clusters, and familiarity with good software engineering practices and advanced data management tools will be highly regarded by the screening committee. Hands-on experience with the development of web apps for visualization of complex solutions is also favorable.
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/IRC30833
Research Scientist in Social Sciences
The Bywater Institute’s mission is to catalyze thriving climate and water futures through transdisciplinary and collaborative scholarship. Housed at Tulane University in New Orleans, LA, the ByWater Institute is committed to making an impact from local to global scales by developing solutions that prioritize equitable access to environmentally safe spaces, connecting communities with the resources they need to thrive in the face of adversity.
Our team is seeking a highly motivated and skilled Social Scientist who will support research on climate adaptation, community-based resilience planning, and socio-economic impacts of environmental changes along the U.S. Gulf Coast and other areas of the globe.
Ideal candidates will have a deep understanding of the interrelations between social and ecological systems and have interest in engaging and collaborating with scholars from a range of disciplinary backgrounds. Candidates should also have experience with qualitative data collection and analysis, as well as excellent leadership and organizational skills. Experience working with communities disproportionately impacted by climate change is a plus.
The incumbent will grow the research capacity of ByWater by designing and implementing innovative research projects. A large portion of the incumbent’s time will be spent supporting ByWater Research Faculty with a National Academies of Sciences, Engineering, and Medicine Gulf Research Program funded study examining drivers of climate and environmental migration and socio-economic resilience in the Lower Mississippi River Delta. Additionally, the incumbent will have opportunities to seek external funding and pursue their own research.
Essential functions of the position include: (1) conducting semi-structured interviews and field observations; (2) managing and analyzing qualitative data; (3) writing articles and grant reports; (4) presenting research results; (5) engaging in research proposal development.
A terminal Degree (Ph.D.) in a social science-related field, or interdisciplinary field with adequate focus on social dimensions of the environment, is required. Additional required and preferred qualifications can be found in the official job description, found here: https://jobs.tulane.edu/position/IRC30831
The following application materials are required:
• Cover letter (2 page maximum)
• CV
• Statement on research experience and interests (3 page maximum)
• 2 academic writing samples
• List of three professional references (letters of recommendation will be required for individuals making it to the interview stage)
For questions about your application please email ByWater@Tulane.edu
Scientific Research Analyst II
The Scientific Research Analyst II position will support the research of the Associate Research Professor of the Tulane ByWater Institute in the fields of socio-hydrology, climate change, and health. The incumbent will work in a multidisciplinary environment assisting advancement of multiple research projects in the fields of the nexus of water-food-energy, climate change and mental health, valuation of flooding and climate change adaptation.
Required Knowledge, Skills, and Abilities
• Experience data management, data analysis/modeling and data visualization/reporting.
• Programming skills using any programing language or coding/developer platform.
• Ability to conduct literature review and to write technical notes/reports.
• Excellent time management, interpersonal, organizational, and communication skills.
Required Education and/or Experience
• Bachelor’s Degree
• At least 2 years of experience in quantitative analysis/modeling.
Preferred Qualifications
• Master’s Degree
• Familiarity with R or Python
• Experience with geospatial data
• Knowledge of statistics and probabilities
• Motivated to embark on multidisciplinary research topics
How to Apply: Please apply electronically at https://jobs.tulane.edu/position/IRC30832. For full consideration, please submit a resume and cover letter that details your research experience and interests.
Research Coordinator
The Research Coordinator will support research on coastal climate adaptation, community and economic resilience, and urban and rural water management at the Tulane ByWater Institute. The incumbent will work in a multidisciplinary environment supporting research staff and faculty in managing, coordinating, and executing research and other funded programming.
Required Knowledge, Skills, and Abilities
• Experience working in scientific teams on research programming.
• Experience working for or collaborating with community organizations or other non-governmental organizations.
• Excellent time management, interpersonal, organizational, written, and communication skills.
• Ability to initiate and cultivate effective partnerships between the university and community organizations.
• Ability to travel locally and regionally.
• Ability to take leadership role in organizing diverse participants to effectively achieve programmatic goals.
Required Education and/or Experience
• Bachelor’s Degree in environmental science and/or environmental social science.
• Two years of experience contributing to externally funded research programs.
Preferred Qualifications
• Experience in multidisciplinary research teams.
• Experience with project management and meeting facilitation.
• Experience working with climate justice and/or Indigenous communities.
• Experience in environmental science field work, data collection, and data management/analysis.
How to Apply: Please apply electronically at https://jobs.tulane.edu/position/IRC30779. For full consideration, please submit a resume and cover letter that details your research experience and interests.
