Traineeship

The SEAS Traineeship is a combination curricular and co-curricular activities designed to complement the research initiatives, resulting in Trainees acquiring skills to effectively communicate, collaborate, and lead interdisciplinary teams. Trainees are required to complete curricular requirements and actively participate in co-curricular activities.

All Trainees are required to take 9 credit hours of core coursework in Materials Science
and Engineering, Mathematics, and Statistics, and at least 3 credit hours in elective
coursework outside of their home discipline.

Core Coursework
MSE/ST 723 Materials Informatics
MSE 710 Elements of Crystallography & Diffraction
MA 540 Uncertainty Quantification for Physical & Biological Models
ST 515 Experimental Statistics for Engineers I (or equivalent, i.e. ST 516 or ST 517)
Elective Coursework
MA 573 Mathematical Modeling of Physical & Biological Processes I
MA 574 Mathematical Modeling of Physical & Biological Processes II
MSE 721 Nanoscale Simulation and Modeling
MSE 791 Density Functional Theory (DFT) in Materials Science and Engineering
ST 533 Spatial Analysis
ST 540 Applied Bayesian Analysis
ST 564 Statistical Thinking and Big Data

Lab Rotations

Statistics PhD Fellow at the Argonne National Lab Advance Photon Source

Goals and Objectives: Lab rotations are an integral component of the SEAS traineeship with goals to broaden trainees understanding of research in various disciplines and to build a community of scientists through interdisciplinary collaborations. All first-year trainees and new Bridge to PhD trainees participate in lab rotations. The objectives are to (i) expose students to scientific procedures and techniques in other disciplines, (ii) increase understanding and knowledge of the contributions of each discipline to data-enabled research at the atomic level, (iii) develop broad-based skill sets (iv) promote collaboration, communication, and community among disciplines and trainees. This experience is structured to provide a glimpse into the research areas of faculty and other SEAS trainees.

 

Research Clusters:

  1. Simulation/Computational: Brenner, Yingling, Patala, Irving, Pasquinelli
  2. Experimental: Dickey, Jones, Ade, Martin, Ford
  3. Stats/Math Analysis: Reich, Smith, Wilson

Structure:

Trainees will actively engage in experimental and analysis processes in the three identified research clusters: simulation/computational, experimental, statistics/mathematics. Each rotation should clearly identify the research questions, provide information in the tools that are used, demonstrate the type(s) of data that is generated, and how data is analyzed. At the end of the rotations, trainees will summarize their experiences in each rotation and present results to the group.

Training Modules

The following tools provide trainees, and those seeking additional background, with information on a number of data science-related topics in experimental and computational materials science, mathematics, and statistics.

Feb 7, 2020 Roger French Case Western University C-Si Photovoltaic Module Degradation across Stressors and Climate Zones: Doing Materials Data Science at Scale with Time-SAERIES AND Image Datasets
Jan 17, 2020 Rampi Ramprasad Georgia Tech Polymer Informatics: Current Status and Critical Next Steps
Dec 9, 2019 Yibin Xu National Institute for Materials Science, Japan Towards Materials Big Data: Expectations and Challenges
Nov 8, 2019 Sergei Kalinin Oak Ridge National Lab A Material Opportunity: How Microscopy and Autonomous Experimentation can Accelerate Materials
Design
Aug 30, 2019 Raymundo Arroyave Director, D3EM NRT and Professor of MSE at Texas A&M 
Beyond High-throughput Materials Discovery: Bayesian Multi-Objective Discovery of Materials under Model Uncertainty and with Multiple Information Sources
Jan 18, 2019 Taylor Sparks Associate Professor at University of Utah Harnessing Data Science to Transform Experimental Materials Discovery
Sept 28, 2018 Michael Demkowicz Associate Professor of MSE at Texas A&M Preferential Degradation of Coherent Twin Boundaries in Ni and Ni-Base Alloys
Sept 22, 2017 Bryce Meredig CEO and co-founder of Citrine Informatics Democratizing Large-Scale Data and Machine Learning in Materials Research
April 10, 2017 Katherine Page Instrument Scientist at Oak Ridge National Lab

A suit of development workshops and seminars are offered to enhance trainee’s skills and career preparation

Research
Elements of Design for Data Visualization workshop, Karen Ciccone, Department Head of Data & Visualization Services for NCSU Libraries
Use of Collaborative Research Tools, Trainee/Fellow Matthew Manning
Establishing Research Collaborations, Faculty Panel
Career Development
Individual Development Plan, Ashleigh Wright
Entrepreneurship, Bryce Meredig CEO and co-founder of Citrine Informatics
Translating Your PhD Skills for Non-Technical Careers, Ashleigh Wright
Non-Technical Career Opportunities at Citrine Informatics, Greg Mulholland, co-Founder of Citrine Informatics
Interdisciplinary Opportunities at Sandia National Laboratories:  A Perspective from an Engineering Scientist, Jordan Massad of Sandia National Laboratory
Internships, Trainee/Fellows Jocelyn Chi and Nicole Creange
Communication
Scientific Storytelling, Ashleigh Wright
Communication Your Science to Diverse Audiences, Graduate School
Elements of Design for Data Visualization, Karen Ciccone, Department Head of Data & Visualization Services for NCSU Libraries
Writing Stellar Research Article Introductions, Dr. Meagan Kittle Autry, Assistant Dean of Academic Affairs at William Peace University and Teaching Assistant Professor at NC State in Civil, Construction, and Environmental Engineering
Writing Retreat, Dr. Katie Homar, Director of International Engineering Writing Support of the NCSU Graduate School
Personal Development
Staying Motivated in Graduate School: How does Happiness Effect Productivity, Dr. Galen Panger of UC Berkeley
Imposter Syndrome
Trainees working on projects during writing retreat
Jordan Massad discussing fellowship opportunities at Sandia National Lab

 

 

Eligibility
Group working on Materials and Data Science Hackathon project
  • Admitted to or enrolled in M.S. or Ph.D. program in materials science and
    engineering, mathematics, statistics, physics, chemistry, textiles engineering &
    chemistry, or other related disciplines
  • Be in good academic standing
  • Plan to conduct research that incorporates data science tools
Benefits
  • New opportunities for interdisciplinary research and entrepreneurial ideas
  • Expanded professional network through interactions with experts in the field
  • Development of transferrable skills applicable to a broad range of career paths

Traineeship Interest Form

A limited number of competitive fellowships are awarded annually to Trainees who demonstrate strong research interests in alignment with the interdisciplinary approach to data science in materials. Fellowships are funded by the National Science Foundation and the Colleges of Engineering and Sciences of North Carolina State University. Fellowship includes a stipend of $34,000 plus tuition and fees* for up to two years.

*Fellowships funded by the NC State Colleges of Engineering and Sciences do not cover fees

Eligibility

  • U.S. citizen or permanent resident
  • Currently enrolled graduate student
  • Active participant as a Trainee for at least one year
  • Students with strong academic record and research progress
  • Students that can contribute to the development and success of SEAS

Application Package

  • Application form
  • Updated CV
  • Letter of support from current research advisor (Emailed directly to seas_graduate_nrt@ncsu.edu)
  • Unofficial transcript
  • Statement of interest (not to exceed 2 pages) that describes (i) your research and career goals; (ii) how your participation and support of the SEAS fellowship will contribute to your goals; and (iii) your contribution to the SEAS program.