Core courses: The core courses offered in the graduate curricula spread across various participating UD departments will serve as a strong disciplinary foundation after which the NRT trainees will be introduced to cross-disciplinary training through elective courses.
NRT specific courses:
- NRT Hackathon Course: The hackathon-style course is mandatory for our NRT (funded and unfunded) trainees after the trainees complete their disciplinary core classes and the two cross-disciplinary electives, and before the trainees’ summer internship/teaching workshop. This hackathon course will be cross-listed as an elective in all the participating trainees’ departments and will be broadly available to graduate students outside of the NRT program. This course will involve teams of 3-4 trainees from different disciplines working together to solve (over the semester) soft materials (MAT) problems submitted by academic labs, industry, and national laboratory using high-performance computing (HPC) and/or data science (DS) tools. Researchers in industry and national lab partners who will provide such MAT problems will also serve as mentors to the teams during this hackathon course. This course will be co-instructed by two NRT core-team faculty with convergent expertise. The co-instructors and industrial/national lab partners mentors will guide the teams through teamwork, collaboration, and oral & written communication exercises. The trainees and enrolled students will a) develop and sharpen their technical skills in MAT, HPC and/or DS applied towards practical problem solving; b) learn to communicate across research expertise; and c) develop strong interpersonal negotiation, teamwork, and collaborative skills.
Elective courses: Guided by their primary and secondary co-advisors, the trainees will select appropriate elective courses from the discipline complementary to their core expertise. For example, a trainee from MSE/CBE/CBC/BME department would select DS and/or HPC electives, while a trainee from CIS/ECE/DS will select MAT focused electives that also cover relevant HPC/DS tools for MAT. The NRT technical training co-directors will work with graduate program directors in various departments to get these electives approved in their graduate curriculum.
The following are existing NRT relevant UD electives that the NRT trainees could choose from during their traineeship:
- ELEG 817 -‘Large scale machine learning’ (Brockmeier): This course prepares students for using ML and DS in a variety of domains by focusing on how to scale up approaches for analyzing real-world data sets. The topics involve computational and statistical aspects from theoretical and practical perspectives. The formative assessments provide exercise in problem formulation, critical reading of literature, algorithm implementation, abstract writing, experimental design, and peer review. MAT domain problems will be included in this course to align this elective with this NRT program.
- CHEG 867 FALL ‘Molecular modeling and simulations of soft materials’ (Jayaraman): This course provides an overview of coarse-grained and atomistic models, molecular dynamics and Monte Carlo simulations in various ensembles, and applications of these models and simulations to study problems in chemical and materials sciences.
- CHEG 867 ‘Process Systems Engineering: Mathematical Modeling and Optimization Principles’ (Ierapetritou): This course provides fundamental instruction on the mathematical programming techniques used in the solution of process design, synthesis and operations problems. This course will review techniques in optimization and mathematical modeling to study synthesis analysis, evaluation, and optimization of process alternatives, process operations involving planning and scheduling, uncertainty considerations on process design and operations.
- MSEG 667- ‘Organic electronics’ (Kayser): This course covers the fundamentals of organic electronics (polymers and small molecules), their synthesis, design principles, structure-property relationships, materials processing and device fabrication. The course includes a section on computational methods (atomistic and coarse-grained modeling and simulations) with guest lecturers which could be expanded further to align with the HPC+DS tools.
- ‘High-Performance Computing (HPC) 101’ (Eigenmann): This is an introductory course to HPC and DS that students across the disciplines take to learn basic principles, terminology, and technology needed to develop and run efficient computational and data science applications.
All UD based data science related courses can be found here as well.
Graduate students can reach out to the indicated course instructors if they have additional questions about the above electives.