Research Scientist

New Orleans, La

Applications have closed

Tulane University

Our Vision: Thriving ByWater Cities, coastlines and river basins around the globe that flex and extend with nature – rather than resisting it – to grow and share sustained prosperity for everyone.

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Research Scientist in Data science

Tulane University’s ByWater Institute invites applications for a staff research scientist in the field of real-time data analytics to begin in 2022. 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 work on big data analytics for surface and subsurface model and data integration.  Much existing research on big data has focused on “big volume”, which has spawned research and implementation on highly scalable, fault-tolerant data processing. There is a recent realization that big data systems should also be able to absorb and process high-volume incoming data in real time or with low latency so that timely information and insights can be derived for critical applications with real-time constraints.  The candidate will investigate the design and optimization of big and fast data analytics systems as applied to collecting, storing and processing field data relevant to monitoring trends and underlying interdependencies in surface and underground water ecosystems across large geographical areas. Work will involve (a) benchmarking large complex spatial-temporal and networked data analytics workloads, (b) innovating in popular large scale data processing and analytics platform, such as Hadoop, Spark, and TensorFlow, with new algorithms for analysis of complex water ecosystems, and (c) optimization under multiple objectives including latency, throughput, and cloud computing cost.


Minimum qualifications

Applicants must have a Ph.D. in Computer Science with a background in high performance computing with strong and demonstrated interest in IoT, Machine Learning, Data integration, and Data Analytics. One or two research papers on related topics, as well as experience of implementation and experimentation with big data systems, are strongly preferred.


Desired qualifications

Hands on experience with development of Web Apps, deployment and management of high performance and data-intensive compute clusters, running large experiments in those clusters, and development of advanced data management tools will be highly regarded by the screening committee.


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 in the area of 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.

Tags: Big Data Computer Science Data Analytics Data management Hadoop HPC Machine Learning Research Spark TensorFlow

Region: North America
Country: United States
Job stats:  401  30  0

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