Research Engineering/ Scientist Assistant - Deep Learning for Structural Biology

Job Posting Title:

Research Engineering/ Scientist Assistant - Deep Learning for Structural Biology ----

Hiring Department:

Oden Institute ----

Position Open To:

All Applicants ----

Weekly Scheduled Hours:

20 ----

FLSA Status:

To Be Determined at Offer ----

Earliest Start Date:

Ongoing ----

Position Duration:

Limited Duration Based on Business Need ----

Location:

UT MAIN CAMPUS ----

Job Details:

General Notes

The Oden Institute is an organized research unit that fosters interdisciplinary programs in computational sciences and engineering, computational medicine, computational geosciences, mathematical modeling, applied mathematics, data science, artificial intelligence, software engineering, and computational visualization.

The University of Texas at Austin is a nationally ranked, tier-one research institution and one of the largest employers in central Texas. UT is located in the heart of Austin, a vibrant city that frequently appears on lists of best cities to live and work. Committed to recruiting and retaining a varied and talented workforce, the university offers competitive salaries and benefits, an extensive support network, and above all, an enriching and highly collaborative community that is deeply passionate about our vision for higher education and public service.

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 Human Resources (HR) website to learn more about the total benefits offered.

NOTE: This position is initially appointed for a six-month term. Continuation beyond the initial six-month assignment is contingent upon funding availability and satisfactory performance.

Purpose

We are inviting highly qualified individual to join our research group at the Oden Institute, University of Texas at Austin. This is a competitive opportunity for individuals with proven deep learning expertise and a strong interest in pioneering applications in computational biology.

Responsibilities

The selected candidate will be involved in the development, implementation, and evaluation of deep learning methods for structural biology and molecular modeling. Depending on experience and project needs, responsibilities may include:

Research

• •Developing and applying deep learning models for predicting protein structures and molecular interactions
• •Exploring model architectures and training approaches, including geometric deep learning, diffusion models, and graph neural networks
• •Preparing and processing biological and structural datasets for model training and evaluation
• •Implementing and maintaining research code and computational workflows
• •Training, fine-tuning, and evaluating models using GPU and high-performance computing resources
• •Designing computational experiments to assess model accuracy, efficiency, and generalization
• •Comparing model performance with existing methods using appropriate benchmarks and evaluation metrics
• •Investigating model limitations and exploring ways to incorporate physical and biological information
• •Reviewing relevant scientific literature and adapting promising methods to ongoing research

Research documentation and communication

Documenting methods and experiments to support reproducibility

Research coordination and collaboration

• •Preparing figures, reports, presentations, and contributions to scientific publications
• •Collaborating with researchers in computational biology, biochemistry, and biophysics to guide model development and interpret results

Required Qualifications

• •Bachelor's Degree in a relevant or related field
• •Strong experience in deep learning, especially in: Geometric Deep Learning
• •Diffusion Models, and Related areas such as graph neural networks, equivariant architectures, and generative modeling
• •Proficient programming skills and familiarity with modern machine learning frameworks

Relevant education and experience may be substituted as appropriate.

Preferred Qualifications

Interest or background in biological or structural applications

Salary Range

$3,000 monthly (50% Full Time Equivalent)

Working Conditions

• •May work around standard office conditions
• •Repetitive use of a keyboard at a workstation
• •Use of manual dexterity

Required Materials

• •Resume/CV
• •3 work references with their contact information; at least one reference should be from a supervisor
• •A brief description of your research interests and relevant experience, and any examples of previous computational or structural biology work.

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.

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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. ----

Background Checks:

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

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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.

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Pay Transparency:

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 wit