Research Engineering/ Scientist Assistant - Deep Learning for Structural Biology

University of Texas; Utah; Presencial; USD 27.000 - 40.000; A tiempo parcial

Descripción de la vacante

The University of Texas at Austin invites applications for a Research Engineering/ Scientist Assistant specializing in deep learning for structural biology. The role focuses on developing and evaluating DL models for protein structures and molecular modeling within the Oden Institute.

The position is 20 hours per week on a limited-duration basis, with a six-month initial term and funding-dependent extension. Collaboration across computational biology, biochemistry, and biophysics is expected.

Formación

• Bachelor's Degree in a relevant or related field.
• Strong experience in deep learning, especially geometric deep learning, diffusion models, 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.

Responsabilidades

• Develop, implement, and evaluate deep learning models for structural biology and molecular modeling.
• Explore architectures including geometric deep learning, diffusion models, and graph neural networks.
• Prepare and process biological/structural datasets for training and evaluation.
• Maintain research code and computational workflows.
• Train, fine-tune, and evaluate models on GPU and HPC resources.
• Design experiments to assess model accuracy, efficiency, and generalization.
• Compare model performance with existing methods using benchmarks and metrics.
• Collaborate with researchers to guide model development and interpret results.
• Document methods and experiments to support reproducibility.
• Prepare figures, reports, and contributions to scientific publications.

Conocimientos

Geometric deep learning

Diffusion models

Graph neural networks

Equivariant architectures

Generative modeling

Programming skills

Educación

Bachelor's degree

Descripción del empleo

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.

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

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.

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.

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 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.

E-Verify

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. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF]

Compliance

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act) and if this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

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