PostDoctoral Research Associate (AI models for visualizing and understanding flood risk)

Texas A&M University
Galveston or Houston, TX, United States
Position Type: 
Postdoc
Organization Type: 
University/Academia/Research/Think tank
Experience Level: 
Not Specified
Degree Required: 
Doctorate

EXPIRED

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Title: PostDoctoral Research Associate

Location: Texas A&M University, Institute for a Disaster Resilient Texas. Galveston and/or Houston Texas.

Time Period: Two years (2020-2022). Position effective immediately will remain open until filled.

Purpose of position: The Institute for a Disaster Resilient Texas (IDRT) is looking for a Postdoctoral Associate to help develop and integrate AI models for visualizing and understanding flood risk. The researcher will contribute to research, development, and support activities involving data mining and machine learning technologies and the interfaces by which users can transparently harness these techniques and technologies. The researcher will coordinate with the IDRT and the UrbanResilience.AI Lab to develop data and model pipelines and integration techniques. The researcher will be supervised by Dr. Sam Bordy, Dr. Ali Mostafavi, and Dr. Wes Highfield.

Minimum Requirements:

Appropriate doctoral degree. If the degree had not been conferred at the time applicant applied for a postdoc position, a letter needs to be attached from the school's official Graduate Office and/or Registrar's Office certifying that all requirements for the degree have been met and stating the degree conferral date.

Essential functions:

·       Responsible for working with data providers, consumers, systems experts, and staff to design, develop, deploy, and support scalable high-performance machine learning applications and systems.

·       Assists fellow research associates, research scientists, engineers, or faculty members with specific phases of research projects.

·       Explore and understand new techniques and technologies related to support high performance scalable data analysis.

·       Assist with creating research and system proposals to support work done at the Institute for Disaster Resilient Texas and the UrbanResilience.AI Lab. 

·       Other related functions as assigned by manager and leadership team.

Required qualifications:

·       Ph.D. in Data Science, Computer Science, Statistics, or other related research field with a strong background in machine learning and data analytics.

·       Experience implementing and supporting a machine learning and data analytics applications or workflows.

·       Good programing skills with the Python programming language.

·       The ability to learn, adapt, and teach new technologies to enable new capabilities or improve on existing ones.

·       Experience collaborating with a team of domain and technology experts implementing solutions based on machine learning techniques.

·       Excellent written and verbal communications skills.

·       Equivalent combination of relevant education and experience may be substituted as appropriate.

Preferred qualifications: One or more of the following qualifications are strongly desired but not required to apply.

·       Two or more years of experience in developing and implementing machine learning algorithm applications in an academic research environment.

·       Two or more years working experiences with high performance computing resources and data centers.

·       Practical experiences with teaching, deployment, and using at least one of common deep learning frameworks such as Caffe, Tensorflow, deeplearning4j, Torch.  

·       Excellent problem solving and strategic thinking skills.

·       Familiar with standard parallel computing tools and interface, such as MPI.

·       Recent peer-reviewed publications in computer science fields relevant to machine learning, big data analysis or high-performance computing.

To be considered for the position, applicants should send a 1) cover letter that clearly articulates how your qualifications and experience make you a viable candidate for this position, 2) a CV, and 3) the names and contact information (including e-mail addresses) of three professional references.

Interested candidates can apply at: https://www.tamug.edu/career/Job-Postings.html, once the job has been formally posted (may take at least a week). In the interim, inquiries can be made to Russell Blessing at russellblessing@gmail.com.