Cross Disciplinary Neural Engineering Training Program

Supported by a T32 grant from the National Institute of Neurological Disorders and Stroke, an institute within the U.S. National Institutes of Health

Understanding how the brain works and treating brain disorders are challenging tasks that need future leaders in neuroscience research. At the Penn State Center for Neural Engineering, we invite bright and ambitious graduate students to join the Cross Disciplinary Neural Engineering (CDNE) training program funded by the National Institutes of Health (NIH). Second-year graduate students associated with the center should apply to the program to participate in their third and fourth years. Through the program, graduate students will learn to work across the disciplinary boundaries of engineering, sciences, mathematics, and human brain health, with the ability to communicate and understand deeply the needs of their collaborators. Ultimately, students who participate in the program will have the opportunity to produce lasting advances in both basic neurosciences and human brain health.

Trainees can acquire:

  • Excellent cross-disciplinary scientific literacy and ability to communicate across boundaries.
  • Ability to lead and work within scientifically diverse groups.
  • Proficiency in advanced data analytic and statistical techniques, as well as experimental design, that span engineering and device development, theoretical modeling, as well as neurophysiology and human brain health applications.
  • Competence in identifying and addressing ethical concerns that might emerge in their research.

Training Curriculum Overview

To join the program, students need to finish two foundational courses and one elective course in their first two years.

Training activities include:

  • All-Group Meeting: A weekly cross-group and cross-disciplinary meetings for monthly programmatic lectures, discussions of ongoing research and experimental and statistical design, and formal presentations
  • Cross-Disciplinary Training Modules
  • Annual Retreat that combines targeted topical lectures and student presentations of their work.

Trainees will be required to participate in the weekly All-Group Meeting; in at least one journal club per semester and lead a journal club for a semester in the second year of the program; participate in the annual retreat; prepare a fellowship grant application by the end of the first year in the program; and complete twelve training modules by the end of the training.

Required Courses

The required courses help prepare the trainees to work at the boundary between engineering, mathematics and neuroscience. Each is three credits and can serve as either required or technical electives in the students’ home graduate programs.

Two of the required courses are foundational and present two different views of topics in neuroscience to provide core language for students working across disciplines.

  • ESC 525: Neural Engineering: Fundamentals of Interfacing with Brain (three credits)
    The course explores theoretically and quantitatively the biophysical basis of neural function, measurable signals, and neural stimulation, and the electrochemical nature of the electrode tissue interface.
  • NEURO 520: Cellular and Molecular Neuroscience (three credits)
    An introduction to neurons, glia, and the molecular basis of brain function.

To prepare the students to work with quantitative and statistically sound approaches, they must complete at least one course on modeling of data through either:

  • ESC 555: Neuroscience Data Analysis (three credits)
    This course covers the biophysical origin and measurement of brain signals, the theoretical background of modern analysis methods and their practical implementation to brain signals, and introduces a toolbox of mathematical and computational techniques to analyze electrophysiological, optical and anatomical data. Topics covered include spectral methods, neural encoding and decoding, information theory and image analysis.
  • STAT 557: Data Mining (three credits)
    This course introduces data mining and machine learning methods, major software packages, and their applications. Topics covered in this course include linear classification/regression, logistic regression, regularization and dimension reduction, decision trees, and mixture models.

Below is a table summary of the required courses and training activities.

summary of required courses and training activities
Course/Activity Topic Years
Foundational Courses (both required) ESC 525: Fundamentals of Interfacing with Brain 1, 2
NEURO 520: Cellular and Molecular Neurosciences
Course in Statistics or Neural Data Analysis (one required) ESC 555: Analysis of Neural Data 1, 2
STAT 557: Data Mining
CDNE Required Activities All Group Meeting: Weekly meeting of trainees, trainers, and statistician. Once per month is a programmatic lecture. Other weeks involve presentations of ongoing research and discussions of experimental design and statistical design. 2-5
CNE Seminar Weekly: Seminar with local and invited faculty presentations. All
Journal Clubs: Cross-group meetings to discuss and provide critical cross-disciplinary interpretation of current scientific literature. JCs are organized around themes such as neurotechnology, computational neuroscience, control theory application, brain-computer interfaces, etc. All
CNE Retreat: Faculty lectures and student presentations. All
CDNE Training Modules Cross Disciplinary Training Modules: Complete at least 3 per semester. 2, 3
Professional Development (both required) Grant Writing: Advanced training and submission of a grant proposal. Informal course. 3
Responsible Conduct of Research (RCR): Penn State offers a Scholarship and Research Integrity Program (SARI) on such topics as publication practices, conflicts of interest, mentoring, research misconduct. 1, 5

Application Process

Interested students should review the CDNE Training Program Guide for Students.  

To apply, Penn State graduate students must submit:

All application materials should be submitted to Corby Williams at

Please note:

  • Prospective engineering graduate applicants should state that they are interested in following the Cross Disciplinary Neural Engineering Training Program.
  • Once matriculated, students should enroll anytime, and ideally within their first two years of graduate school.
  • Those in year two of their graduate studies can be nominated for fellowship support. The deadline for funding is Feb 15 annually.

Current graduate programs contributing to the CDNE training program include:

  • Anthropology
  • Biomedical Engineering
  • Electrical Engineering
  • Engineering Science and Mechanics
  • Mathematics
  • Mechanical Engineering
  • Physics
  • Neuroscience

Additional programs may be added under advisement of the CDNE faculty (and their service on graduate programs) to the program administration.


Email Bruce Gluckman, CDNE Director, or Dezhe Jin, CDNE Associate Director


c d n e training fellows contact us


The Penn State Center for Neural Engineering is a large, interdisciplinary research group that brings together neural engineering-focused researchers from the Penn State College of Engineering, the College of Medicine, the Materials Research Institute, and the Eberly College of Science. Chartered in June 2007, the center occupies 22,000 square feet of space in the Millennium Science Complex.

Center for Neural Engineering

Millennium Science Complex

The Pennsylvania State University

University Park, PA 16802