Research Associate for Artificial Intelligence and Machine Learning

Job Details

General Notes

The Texas Advanced Computing Center (TACC) at The University of Texas at Austin is one of the leading supercomputing centers in the world, supporting advances in computational research by thousands of researchers and students.

TACC staff help researchers and educators use advanced computing, visualization, and storage technologies effectively, and conduct research and development to make these technologies more powerful, more reliable, and easier to use.

TACC staff also educate and train the next generation of researchers, empowering them to make discoveries that advance knowledge and change the world.

If you are not sure that you’re 100% qualified, but up for the challenge we want you to apply. We believe skills are transferable and passion for our mission goes a long way.

fosters a culture of innovation, passion, and fun by encouraging staff members to actively collaborate to investigate the latest technologies, team up for charities, and celebrate successes together.

TACC promotes a healthy workplace by helping employees achieve balance between their personal and professional lives to increase employee engagement, job satisfaction, and overall well-being.

Candidates will need to upload a resume, letter of interest, and the names of three references to apply for this position.

UT Austin offers a competitive benefits package that includes :

• 100% employer-paid basic medical coverage
• Retirement contributions
• Paid vacation and sick time
• Paid holidays

Please visit our website to learn more about the total offered.

Purpose

The Research Associate will work in the Scalable Computational Intelligence group as they support researchers leveraging modern AI / ML techniques.

The ideal candidate will have a strong background in data analytics and a passion for research across many science and engineering domains.

Responsibilities

• Consult and work with data providers, analysts, systems experts, and other research staff to design, develop, and deploy machine learning and data analytics systems supporting defined project requirements.
• Mentor TACC staff in machine learning and data analysis techniques and technologies and the support needed for them to work within an HPC cluster environment.
• Support the application of AI / ML techniques across various set of topics and domains.
• Support training of AI / ML techniques and best practices to a broad range of researchers
• Collaborate and propose new funding opportunities supporting research done at TACC.
• Prepare reviewed papers, technical reports, design, and requirements of data analytic techniques and systems, optimizations, and novel applications across domains supported at TACC.
• Stay at the forefront of new techniques and technologies applicable to AI / ML systems that support implementations in various science and engineering domains.
• Other related functions as assigned.

Required Qualifications

• Ph. D. in science, engineering, or other related research fields with a strong background in applied data analytics techniques for research.
• Experience working with AI / ML platforms and algorithms.
• Experience working with domain experts, researchers, and stakeholders to support different applications for their data analytics needs.
• The ability to learn and adapt new technologies to enable new capabilities or improve existing ones.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience in analyzing both measured and simulated data sources for scientific and engineering research.
• Experience supporting and extending open-source and open-data products for different research communities.
• Familiarity with data analysis systems and workflows.
• Experience training and mentoring researchers in best practices when creating data workflows.
• Strong problem-solving and strategic thinking skills.

Salary Range

$90,000 + depending on qualifications

Working Conditions

• Typical Office Environment
• Repetitive use of a keyboard

Required Materials

• Resume / CV
• 3 work references with their contact information; at least one reference should be from a supervisor
• Letter of interest

Important for applicants who are NOT current university employees or contingent workers : You will be prompted to submit your resume in the first step of the online job application process.

Then, any additional Required Materials will be uploaded in the My Experience section; you can multi-select the additional files or click the Upload button for each file.

Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers : As a current university employee or contingent worker, you MUST apply within Workday by searching for Find Jobs.

Before you apply though, log-in to Workday, navigate to your Worker Profile, click the Career link in the left-hand navigation menu and then update the sections in your Professional Profile.

This information will be pulled in to your application. The application is one page and you will need to click the Upload button multiple times in order to attach your Resume, References and any additional Required Materials noted above.

Employment Eligibility :

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers.

Staff who are promotion / transfer eligible may apply for positions without supervisor approval.

Retirement Plan Eligibility :

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.

Background Checks :

A criminal history background check will be required for finalist(s) under consideration for this position.