Job Posting Title:
Texas Advanced Computing Center
Position Open To:
Weekly Scheduled Hours:
Earliest Start Date:
Expected to Continue
PICKLE RESEARCH CAMPUS
The Research Associate is a position in the Scalable Computational Intelligence (SCI) group at TACC. The individual 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.
Responsible for assignments that require the technical knowledge to modify or adapt routine procedures under the direction of a supervisor, to meet special research requirements for data projects hosted at TACC.
Working with data providers, consumers, systems experts, and staff to design, develop, deploy, and support machine learning and other data analytics applications and systems at TACC.
Prepare technical reports, design, and requirements for data analytics applications using machine learning, database, or other data analysis algorithms.
Prepare user documentation and guides to support the usage and operations of data systems and APIs supported and/or developed at TACC.
Assists fellow research associates, research scientists, engineers, or faculty members with specific phases of research projects.
Explore and understand new techniques and technologies applicable to data processing and machine learning for researchers using TACC facilities.
Assist with creating research and system proposals to support work done at TACC and with research partners of TACC.
Train and teach the capabilities and potential usages of machine learning techniques in a wide array of research domains and levels of computational abilities.
Other related functions as assigned.
Experience implementing and supporting a machine learning or data analytics applications or workflows on large scale shared parallel infrastructure.
The ability to learn, adapt, and teach new technologies to enable new capabilities or improve on existing ones based on the needs of researchers
Experience collaborating with a team of domain and technology experts implementing solutions based on machine learning and other data mining and analysis techniques.
Excellent written and verbal communications skills.
Relevant education and experience may be substituted as appropriate.
One or more of the following qualifications are strongly desired:
PhD or 2 years post Master’s Degree in Data Science, Computer Science, Statistics, or other related research field with a strong background in machine learning and data analytics.
Experience teaching the use of machine learning techniques in software systems such as TensorFlow, Pytorch, R, and/or Python.
Experience developing applications in large parallel environments using tools such as Hadoop, Spark, CUDA, and/or MPI.
Experience with shared storage systems and optimizing data workflows in such specialized systems.
Experience teaching machine learning and programming to a wide array of students, researchers, and developers.
Excellent problem solving and strategic thinking skills.
$85,000 + depending on qualifications
May work around standard office conditions
Repetitive use of a keyboard at a workstation
Use of manual dexterity
Letter of interest
3 work references with their contact information including email addresses; References submitted should be professional and not personal and at least one reference should be from a supervisor
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. 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 UT Jobs. If you are a current University employee, 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 before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
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.
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer:
The University of Texas at Austin, as an equal opportunity/affirmative action employer , complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197.