Computational Research Scientist, Biomedical Engineering

University of Texas Southwestern Medical Center • Dallas, TX

35d

About The Position

With over 75 years of excellence in Dallas-Fort Worth, Texas, UT Southwestern is committed to excellence, innovation, teamwork, and compassion. As a world-renowned medical and research center, we strive to provide the best possible care, resources, and benefits for our valued employees. Ranked as the number 1 hospital in Dallas-Fort Worth according to U.S. News & World Report, we invest in you with opportunities for career growth and development to align with your future goals. Our highly competitive benefits package offers healthcare, PTO and paid holidays, on-site childcare, wage, merit increases and so much more. We invite you to be a part of the UT Southwestern team where you'll discover a culture of teamwork, professionalism, and a rewarding career! JOB SUMMARY Leads the design and implementation of High-Performance Scientific Computing infrastructure. Performs and supports scientific research using technical knowledge of software, software development, complex databases, high-performance computing, and/or hardware in a complex computing environment. Performs scientific research using knowledge of formalisms and algorithms from mathematics, statistics, and/or computer science. The Deep Learning for Precision Health Lab (www.montillolab.org ), a part of the Biodata Engineering Program of the Biomedical Engineering department at the University of Texas Southwestern in Dallas, TX seeks a talented and motivated Data Scientist to support large-scale multimodal neuroimaging and biomedical data analysis initiatives and to support advanced AI. The successful candidate will play a key role in curating and analyzing multimodal datasets, preparing resources for foundation-model development, and supporting NIH-funded projects at the intersection of machine learning, medical image analysis, neuroscience, and oncology. This is a full-time, long-term staff scientist position focused on technical excellence, reproducible data management, and collaborative research in a dynamic academic environment. The successful candidate's work will directly inform AI-driven discovery in neurological and oncologic diseases. Our lab's focus is on developing the theory and application of deep learning (DL) and causal modeling to elucidate treatment mechanisms, and to guide prognosis and treatment decisions with applications in neurological disorders and cancer therapy. To maximize AI's efficacy for biomedicine we develop new methods delivering: (1) trustworthy AI, (2) multimodal data fusion, (3) causal analysis, (4) and sample efficiency. We then apply these methods in (5) biomedical and neuroscience applications The successful applicant will contribute by learning to (a) curate and prepare data, (b) apply advanced ML-based causal analysis to quantify mechanistic relationships in experimental data, (c) constructing reinforcement learning approaches to guide dynamic therapies, and (d) assisting in foundation model development and downstream tailoring in biomedical projects. With cutting-edge computational infrastructure, access to leading neurology, neuroscience, and cancer experts, and an unparalleled trove of high dimensional imaging and multi-omic data, our machine learning lab is poised for success in these research endeavors. Primary responsibilities include: curate and manage large neuroimaging & bioimaging datasets that include structural, diffusion, and functional MRI, dynamic PET, EEG, fluorescence microscopy, and multi-omic or clinical data drawn from NIH-supported consortia; develop and maintain automated pipelines for data quality control, and reproducibility; write scripts to characterize dataset structure, imaging quality, and subject longitudinal counts; identify synergies across major repositories to enable integrated AI model training; search public resources for complementary data; and, support preparation of tables, figures, and analyses for grant proposals and publications. Other responsibilities include: clean and prepare datasets for downstream ML and deep-learning workflows; configure and run existing foundation or large-scale deep learning models for benchmarking; and, contribute to manuscript writing and code documentation.

Requirements

• PhD In Computer Science, Electrical Engineering, or related field or
• Master's Degree In Computer Science, Electrical Engineering, or related field
• 2 years Experience in software development/engineering and/or HPC systems management with PhD. or
• 4 years Experience in software development/engineering and/or HPC systems management with Master's Degree.

Nice To Haves

• Candidates must have existing mastery of one or more mainstream DL frameworks (e.g., PyTorch, TensorFlow) and be able to explain intricacies of the DL models they have constructed.
• Candidates must have mastery of Python programming and Linux/Bash scripting.
• Experience running and managing batch jobs on SLURM or similar HPC systems.
• Demonstrated problem-solving capability and ability to learn new technical concepts quickly.
• Highly motivated, detail-oriented, and collaborative; thrives in a multidisciplinary team.
• Candidates must have extensive neuroimaging and biomedical image analysis experience.
• Familiarity with neuroimaging data formats (DICOM, NIfTI, HDF5, MP4, EEG) and web-scraping or data-discovery scripting.
• M.S. or Ph.D. in Computer Science, Biomedical Engineering, Electrical, Physics, Statistics or a closely related computational field.
• Strong foundation in data science, scientific computing, and version control (Git).
• Experience running and managing batch jobs on SLURM or similar HPC systems

Responsibilities

• curate and manage large neuroimaging & bioimaging datasets that include structural, diffusion, and functional MRI, dynamic PET, EEG, fluorescence microscopy, and multi-omic or clinical data drawn from NIH-supported consortia
• develop and maintain automated pipelines for data quality control, and reproducibility
• write scripts to characterize dataset structure, imaging quality, and subject longitudinal counts
• identify synergies across major repositories to enable integrated AI model training
• search public resources for complementary data
• support preparation of tables, figures, and analyses for grant proposals and publications
• clean and prepare datasets for downstream ML and deep-learning workflows
• configure and run existing foundation or large-scale deep learning models for benchmarking
• contribute to manuscript writing and code documentation
• Designs and implements the high-performance scientific computing infrastructure for for large, multi-institution projects, including software applications, workflows, servers, databases, long-term and short-term storage solutions, and cloud services.
• Serves as a domain expert for research and industry collaborations on biomedical computational methods, algorithms and platforms. Subject matter expert in multiple areas of computing technology.
• Leads and develops proof-of-concepts (PoCs) for solutions applied to biomedical and clinical science use-cases by working closely with industry collaborators.
• Designs, develops, implements and optimizes algorithms and scientific workflows for high dimensional data in biomedical and clinical sciences.
• Develops software and methods to explore, analyze and visualize biological data sets.
• Demonstrates and craft applications of cutting-edge visual computing technologies and connect them to core infrastructure.
• Report research results in journal articles, conference papers, and technical manuals.
• Mentor and train staff, postdoctoral fellows, and graduate students.
• Supports faculty and students in adapting computational strategies to leverage high-performance computing, working with a range of systems and technologies such as compute cluster, parallel file systems, high speed interconnects, GPU-based computing and data servers.
• Performs other duties as needed

Benefits

• PPO medical plan, available day one at no cost for full-time employee-only coverage
• 100% coverage for preventive healthcare-no copay
• Paid Time Off, available day one
• Retirement Programs through the Teacher Retirement System of Texas (TRS)
• Paid Parental Leave Benefit
• Wellness programs
• Tuition Reimbursement
• Public Service Loan Forgiveness (PSLF) Qualified Employer
• Learn more about these and other UTSW employee benefits!

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Educational Services
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees