Program Overview

Program Overview

The Rehabilitation Science PhD program at the University of California San Francisco (UCSF) is designed to develop independent investigators in rehabilitation science with innovative, multidisciplinary approaches to the field. The program addresses the broader perspective of basic and clinical sciences in two research tracks: Neuroscience and Musculoskeletal Biomechanics.

Program Description

As the population continues to age, new research in the interdisciplinary field of rehabilitation is essential to support the healthcare needs of society. With this increased demand, rehabilitation scientists with advanced research training are in critical demand to evaluate the effectiveness of current injury prevention and rehabilitation science, and to develop new potential injury prevention and rehabilitation strategies.

Program Leadership

• Richard Souza, PT, PhD, Program Director, Musculoskeletal Biomechanics Lead
• Valerie Block, PT, DPTSc, Assistant Professor, Neuroscience Lead

Program Description

The UCSF Department of Physical Therapy and Rehabilitation Science, in collaboration with faculty from the Department of Physical Therapy at San Francisco State University (SFSU), provides a unique opportunity to study rehabilitation science.

Specialization Areas

• Musculoskeletal Biomechanics: Students are trained on the latest advancements in musculoskeletal biomechanics and are prepared for careers in research in academia and industry. Areas of study include:
  • Assessment of normal and pathological human movement using motion analysis and wearable sensors
  • Robotics for gait augmentation
  • Functional assessments in performance and quality of life measures
  • Advanced quantitative imaging of bone, muscle, cartilage, and tendons
• Neuroscience: Students can follow a clinically-based pathway focused on neural injury and neurodegenerative disease, or laboratory-based bench science research that focuses on experimental models of neurodegeneration and chronic neuroinflammation. Areas of study include:
  • Assessment of activity and function in patients with neurodegenerative disorders
  • Development of new tools for remote movement evaluation
  • Evaluation of motor outcomes following interventions

Admission Requirements

For information about application requirements, please refer to the program's admissions page.

Learning Outcomes

The objective of this program is to develop independent investigators in rehabilitation science with innovative, multidisciplinary approaches to the field. The methods and strategies used for dissertation work will vary depending on the student’s focus and needs. The core curriculum is designed to provide broad training in rehabilitation science. Elective and lab activities will be tailored to create an individualized plan for each trainee.

Degree Requirements

• Minimum GPA of 3.0
• Pass all core courses and required activities
• Complete six quarters in residence, including a minimum of three registered quarters after advancement to candidacy
• Pass the qualifying examination
• Complete and submit a dissertation
• For additional details, please see the graduate program requirements.

Core Courses

Musculoskeletal Biomechanics Track

• REHAB SCI 200A: Laboratory Rotation I (3 units)
• REHAB SCI 200B: Laboratory Rotation II (3 units)
• REHAB SCI 200C: Laboratory Rotation III (3 units)
• REHAB SCI 201: Introduction to Rehabilitation Science (2 units)
• REHAB SCI 202: Gross and Regional Anatomy (1 unit)
• REHAB SCI 203: Doctoral Colloquium (1 unit)
• REHAB SCI 204: Application of Principles of Learning (3 units)
• GRAD 202: Racism in Science (3 units)
• GRAD 214: Responsible Conduct of Research and Rigor & Reproducibility (1.5 units)
• BIOSTAT 200: Biostatistical Methods in Clinical Research I (3 units)
• Second statistics course appropriate to research goals
• Foundational Courses:
  • REHAB SCI 205: Biomechanics of Human Motion (2 units)
  • BIOENGR 221 (2.5-3 units)
• Total Units: 28-28.5

Neuroscience Track

• REHAB SCI 200A: Laboratory Rotation I (3 units)
• REHAB SCI 200B: Laboratory Rotation II (3 units)
• REHAB SCI 200C: Laboratory Rotation III (3 units)
• REHAB SCI 201: Introduction to Rehabilitation Science (2 units)
• REHAB SCI 202: Gross and Regional Anatomy (1 unit)
• REHAB SCI 203: Doctoral Colloquium (1 unit)
• REHAB SCI 204: Application of Principles of Learning (3 units)
• GRAD 202: Racism in Science (3 units)
• GRAD 214: Responsible Conduct of Research and Rigor & Reproducibility (1.5 units)
• BIOSTAT 200: Biostatistical Methods in Clinical Research I (3 units)
• Second statistics course appropriate to research goals
• Foundational Courses:
  • NEUROSCI 201A: Basic Concepts in Cellular and Molecular Neuroscience (5 units)
  • NEUROSCI 201B: Basic Concepts for Cellular and Developmental Neuroscience (4 units)
  • ANATOMY 207: Neuroscience (Audit) (3 units)
• Total Units: 35.5

Additional Statistics Courses

Please select an additional statistics course from the subject areas below.

BIOSTATISTICS

• BIOSTAT 208: Biostatistical Methods II (3 units)
• BIOSTAT 209: Biostatistical Methods III (3 units)
• BIOSTAT 210: Biostatistical Methods IV (2 units)
• BIOSTAT 211: Mathematical Foundations of Biostatistics (2 units)
• BIOSTAT 212: Introduction to Statistical Computing in Clinical Research (1 unit)
• BIOSTAT 215: Strengthening causal inferences based on observational data (3 units)
• BIOSTAT 272: Foundations in Biostatistical Principles and Methods (4 units)
• BIOSTAT 273: Introduction to Biostatistics (0.5 units)

DATASCIENCE

• DATASCI 202: Opportunities and challenges of complex biomedical data (3 units)
• DATASCI 213: Programming for Health Data Science in R (2 units)
• DATASCI 214: Programming for Health Data Science in R II (2-3 units)
• DATASCI 216: Machine Learning in R for the Biomedical Sciences (3 units)
• DATASCI 217: Introduction to Python and Data Science Tools (1-2 units)
• DATASCI 220: Data Science Program Seminar I (1 unit)
• DATASCI 221: Data Science Program Seminar II (1 unit)
• DATASCI 222: Data Science Capstone Project (8 units)
• DATASCI 223: Applied Data Science with Python (2 units)
• DATASCI 224: Understanding Machine Learning: From Theory to Applications (3 units)
• DATASCI 226: Bayesian Methods and Gaussian Processes (2-3 units)
• DATASCI 300: Data Science Educational Practice (2 units)