GRADUATE ASSISTANTSHIPS: FUNDING OPPORTUNITIES
What is an Assistantship?
In engineering, a Graduate Assistantship (GA) is the most common way for students to fund their graduate studies. With a GA, the student is paid a stipend and perhaps tuition support in exchange for research or teaching assistant work. Research is normally done on the topic that is funded by an external agency or internally by 草榴社区, and on which the student will study to earn their MS or PhD. A teaching assistant might teach part of a lecture class or run problem sessions, hold office hours, organize and run a laboratory course, or grade for a professor. At some schools, the GA is labeled GRA if it applies to research. At 草榴社区, we use GA as the research assistantship and TA as the teaching assistantship. GAs are awarded based on the student鈥檚 interest in the research and their qualifications. TAs are awarded based on the student鈥檚 expertise and qualifications.
Eligibility
Awarding of all assistantships is handled by the academic departments and programs. There are no additional requirements beyond what the college or departments outline in their admissions criteria. Eligibility is limited to full-time students admitted for graduate degree programs; non-degree students (those students in the ICA program and not yet matriculated) and part-time students are not eligible. A GA must be enrolled in at least one course during the fall, spring, and summer semesters.
External Fellowships
A variety of fellowships funded by external organizations are also available (DOE, NSF, and SMART fellowships, private foundations, etc.). Information can be found at the websites of these organizations. Students should apply directly to the specific organization awarding a particular external fellowship.
Professional Development for PhD Students
If awarded a GA, in addition to stipend and tuition, the College of Engineering at 草榴社区 has a unique professional development (PD) program for all PhD students. All full-time, post-qual PhD students are required to take at least one PD course. In addition, through additional courses and activities, a PhD student can earn a valuable certificate as a Teaching Scholar or an Industry Scholar. The certificate and PD courses are recorded on the student鈥檚 transcript and are highly valued by academia and industry in the job-search process. See further details here.
GRADUATE STUDENT OPEN POSITIONS
Updated 4/26/2024
GRADUATE ASSISTANTSHIPS IN CIVIL AND ENVIRONMENTAL ENGINEERING
Department: Civil and Environmental Engineering
Category: Graduate Assistantship
Student Classification: PhD
Discipline Area: Water Resources and Environmental Engineering
Project Title: Cross-Sectoral Water and Environmental Systems
Description: The project is in the area of water and environmental systems. Potential topics may include food-energy-water systems, microplastics and emerging contaminants in green stormwater infrastructure, and watershed systems analysis. The student and Dr. Good will determine a thesis topic together that may entail lab or field analysis and/or modeling. By joining the Good Research Group, the candidate will work within a team of other graduate students and undergraduate researchers and be involved with the 草榴社区 Center for Resilient Water Systems.
Qualifications for Appointment: The candidate should have their BS or MS in Civil Engineering, Environmental Engineering, or related field (must be completed at the time of hire). Applicants should be self-motivated and curious, have excellent communication skills, and be willing to work in a collaborative environment. Experience with GIS, programming (Python, R, others), and/or laboratory work are valued and depend on the project but not required. Preference will be given to applicants who already have their M.S. in a relevant field.
Start Date: Spring, Summer, Fall 2024
PI: Kelly Good,
Lab:
Department: Civil and Environmental Engineering
Position number: 22-BMW-1
Category: Graduate Assistantship
Student Classification: Master鈥檚 or PhD
Discipline Area: Water Resources
Project Title: Long-term Monitoring and Analysis of Green Stormwater Infrastructures
Description: The project is continuing a long history of monitoring a bio-infiltration rain garden and a constructed stormwater wetland. The project entails maintaining the monitoring program and analyzing associated data. Because of the long data record, the student and advisor will determine a thesis topic together that may entail additional lab or field analysis and/or modeling. The candidate will work within a team of other graduate students and undergraduate researchers on this stormwater research project and be involved with the 草榴社区 Center for Resilient Water Systems.
Qualifications for Appointment: The candidate should have their BS or MS in Civil Engineering or related field (must be completed at the time of hire). The work will involve field and laboratory research which requires an ability and willingness to do physical work (potentially including entering manholes and the actual stormwater control measures). Additional work will include data analysis and potentially modeling.
Start Date: 06/01/2023
PI: Bridget Wadzuk;
GRADUATE ASSISTANTSHIPS IN ELECTRICAL AND COMPUTER ENGINEERING
Department: Electrical and Computer Engineering聽
Category: Research Assistantship聽
Student Classification: 笔丑顿听
Discipline Area: Radar Signal Processing and Comnmunication Systems聽
Project Title: Integrated Sensing and Communications聽
Description: The research project involves the applications of signal processing algorithms and machine learning methods in the area of integrated radar and communications聽
Qualifications for Appointment: candidate should have a BSc and MSc in Electrical and Computer Engineering with high grades (GPA above 3.5) and have taken courses in signal processing, digital communications, and machine learning. Evidence and a list of prior publications should be provided. Having background in radar is preferred.听听
Start Date: 09/1/2023听
笔滨:鈥�Moeness Amin
Lab:鈥�Radar Imaging Lab听听
Department: Electrical and Computer Engineering
Category: Research Assistantship
Student Classification: PhD
Discipline Area: Signal Processing and Communications
Project Title: Interference-aware Constellation Design
Description: This research project will develop innovative signal transmission constellation for non-orthogonal multiple access (NOMA) channels using deep learning and signal processing techniques.
Qualifications for Appointment: The candidate should hold a BSc and MSc degree in Electrical and Computer Engineering or a related field. They should also possess a strong background in at least one of the following areas: signal processing, communications, or machine learning.
Start Date: Fall 2023 or Spring 2024
PI:
Lab:鈥�Wireless Networking Lab
GRADUATE ASSISTANTSHIPS IN MECHANICAL ENGINEERING
Department: Mechanical Engineering
Category: Graduate Assistantship
Student Classification: PhD
Discipline Area: Sustainability, Thermal Fluids Engineering
Project Title Data centers and Environmental Sustainability
Description: We are looking for a talented, motivated student to join our research center on Energy-Smart Electronic Systems (ES2) in Fall 2024, with a specific focus on predicting and mitigating the environmental burden associated with the data center industry.
Qualifications for Appointment: The student must have a Masters Degree in Mechanical Engineering with research experience in numerical or theoretical modeling, scientific publications (thesis and/or archival journal or conference publications), and a passion for promoting sustainability.
Start Date: 08/15/2024
PI: Aaron Wemhoff
Lab: Center for Energy-Smart Electronic Systems (ES2): Center for Energy Smart Electronic Systems (ES2)
Department: Mechanical Engineering
Category: Graduate Assistantship
Student Classification: PhD
Discipline Area: Mechanics of Materials, Machine Learning
Project Title: First-principles calculations of advanced structural materials for fusion energy applications
Description: The student will work closely with Dr. David Cereceda to conduct first-principles density functional theory calculations of structural materials exposed to extreme environments. Qualifications for Appointment: The ideal candidate is expected to have a solid background in solid-state density-functional theory computations, machine learning and Python programming. To apply, please send a cover letter outlining your qualifications, research experience and interests, CV, and a list of three references to Dr. David Cereceda. The evaluation process will start immediately and will continue until the position is filled.
Start Date: 01/15/2023
PI: David Cereceda
Lab: Multiscale Modeling of Materials and Machine Learning Laboratory (M4L Lab):
Department: Mechanical Engineering
Category: Graduate Assistantship
Student Classification: PhD
Discipline Area: Mechanics of Materials, Machine Learning
Project Title: Physics-informed machine learning models of bio-inspired materials
Description: The student will work closely with Dr. David Cereceda to design bio-inspired structural materials with superior properties.
Qualifications for Appointment: The ideal candidate is expected to have a solid background in computational mechanics, FEM modeling, machine learning and Python programming. To apply, please send a cover letter outlining your qualifications, research experience and interests, CV, and a list of three references to Dr. David Cereceda. The evaluation process will start immediately and will continue until the position is filled.
Start Date: 01/15/2023
PI: David Cereceda
Lab: Multiscale Modeling of Materials and Machine Learning Laboratory (M4L Lab):
Above are the currently available GA openings in the College of Engineering. Please contact the PI if interested. Students are also encouraged to contact faculty members to explore other opportunities for research and possible GA funding, TA work, and scholarships. Several specific qualifications for appointment are provided below. Others appear under Guidelines and Responsibilities for Graduate Students. Please review all qualifications for appointment before contacting a faculty member.