Research Associate 2, GAB

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The Data Science and Computational Biology (Aguiar-Pulido) Lab in the Department of Computer Science at the University of Miami seeks a Research Associate 2 (RA2) to lead and execute studies on interpretability and human-alignment of deep learning models across natural and medical imaging, and multimodal settings. The RA2 must have strong working skills in machine learning, including deep learning, python programming and using PyTorch, and the ability to quickly learn in a fast-changing environment. They will design and run rigorous experiments, create and curate evaluation datasets with appropriate quality control, define task-specific evaluation metrics, and train, fine-tune, and benchmark state-of-the-art machine learning models. They will deliver maintainable, well-documented research software (modular code, tests, and clear docs) following Git-based workflows, manage experiments on Linux/HPC resources with strong attention to reproducibility (environment specs, seeding, data/version control), and communicate results through figures, tables, and concise written summaries. The RA2 will be involved in training of graduate and undergraduate students, and is expected to contribute to manuscript preparation and grant writing. The successful RA2 will be comfortable working in an interdisciplinary team and will be able to adjust the delivery of results based on the audience (e.g., computer scientists, clinicians, molecular biologists). The RA2 must have good organizational skills, an attention to detail, good time management skills and a strong work ethic.

Degree in Computer Science or a related area preferred.

This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.

CORE QUALIFICATIONS

Education:
Bachelor’s degree in relevant field required

Experience:
Minimum 2 years of relevant experience, or M.S. and 1 year of relevant experience

Certification and Licensing:
Not Applicable

Knowledge, Skills and Abilities:

Required

• Hands-on experience training and evaluating deep learning models (including Vision Transformers) and strong proficiency in Python and PyTorch.
• Ability to design, implement, and ship maintainable research software (modular code, documentation, unit tests) and fluency with version control (Git) and collaborative workflows (branches, pull requests).
• Solid grounding in data curation and labeling pipelines (dataset definition, quality control, reproducibility), plus experience building task-specific evaluation metrics.
• Ability to exercise sound judgment and autonomy in designing experiments and interpreting results.
• Excellent organizational skills and attention to detail, strong problem-solving and critical-thinking abilities, and capacity to plan and prioritize daily tasks.
• Proven ability to work both independently on complex tasks and effectively in interdisciplinary environments.
• Strong communication skills and ability to work as part of a collaborative team involving people from different disciplines (e.g., computer scientists, clinicians, molecular biologists).
• Participates in the publication of significant research results and in writing grant proposals.

Preferred

• Experience with eXplainable AI (XAI), especially evaluating alignment of explanations with human-understandable concepts in image analysis and Transformer-based models.
• Exposure to full-stack development and secure, multi-team integrations. Practical NLP experience (e.g., QA with Transformer models such as GPT-2/GPT-Neo/T5) and multi-task generalization.
• Familiarity with Hugging Face, timm, OpenCV, and scientific Python (NumPy/Matplotlib) libraries for end-to-end experimentation.
Experience running model development on Linux and working with high performance clusters. Desire to mentor junior members of the lab.

The University of Miami is an Equal Opportunity Employer - Females/Minorities/Protected Veterans/Individuals with Disabilities are encouraged to apply. Applicants and employees are protected from discrimination based on certain categories protected by Federal law. Click here for additional information.

Job Status:

Full time

Employee Type:

Staff

Pay Grade:

A6