Computational Scientist, Psychiatry — Data & HPC for Biomed

The University of Texas Southwestern Medical Center; Dallas (TX); On-site; USD 95,000 - 130,000; Full time

Benefits offered by this job

PPO medical plan

Paid Time Off

Public Service Loan Forgiveness

Job summary

The University of Texas Southwestern Medical Center in Dallas is seeking a Computational Scientist to provide computational solutions for biological and clinical researchers, utilizing HPC resources, parallel computing, and large-scale data analysis.

This role supports the Computational Psychiatry Laboratory led by Dr. Abigail Ortiz, applying computational methods to mood disorders using wearable data and diverse data sources to accelerate scientific discovery.

Qualifications

• Bachelor's degree in CS/Engineering/Physics or related field.
• Experience with HPC and publishing computational results.

Responsibilities

• Support faculty and research staff in adapting computational strategies to domain needs.
• Work with HPC, parallel file systems, interconnects, GPU computing, and databases.
• Develop software to explore, analyze, and visualize complex biological and biomedical data.
• Participate in design and execution of collaborative research studies in biomedical science.
• Assist research team with computational systems to accelerate discovery.
• Performs other duties as assigned.

Skills

HPC

Parallel computing

GPU computing

Data visualization

Biological data analysis

Education

Bachelor's degree in CS/Engineering/Physics or related

Tools

High-performance compute cluster

Parallel file systems

GPU-based computing

Database servers

Job description

The University of Texas Southwestern Medical Center in Dallas is seeking a Computational Scientist to provide computational solutions for biological and clinical researchers, utilizing HPC resources, parallel computing, and large-scale data analysis.

This role supports the Computational Psychiatry Laboratory led by Dr. Abigail Ortiz, applying computational methods to mood disorders using wearable data and diverse data sources to accelerate scientific discovery.