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Fellowships & Scholarships

POSTDOCTORAL FELLOWSHIP : STANFORD MOLECULAR IMAGING SCHOLARS (SMIS) PROGRAM

PROGRAM DIRECTOR: Craig Levin, PhD, Professor of Radiology

The Stanford Molecular Imaging Scholars (SMIS) program is an integrated, three-year cross-disciplinary postdoctoral training program at Stanford University that brings together 33 faculty mentors from 14 departments in the Schools of Medicine, Engineering, and Humanities and Sciences. Molecular imaging, the non-invasive monitoring of specific molecular and biochemical processes in living organisms, continues to expand its applications in the detection and management of cancer. SMIS faculty mentors provide a diverse training environment spanning biology, physics, mathematics, biocomputation/biomedical informatics, engineering, chemistry, biochemistry, cancer biology, immunology, and medical sciences. The centerpiece of the SMIS program is the opportunity for trainees (PhD or MD with an emphasis on PhD) to conduct innovative molecular imaging research that is co-mentored by faculty in complementary disciplines. SMIS trainees also engage in specialized coursework, seminars, national conferences, clinical rounds, including ethics training and the responsible conduct of research. The three-year program culminates with the preparation and review of a mock NIH grant proposal, in support of trainee transition to an independent career in cancer molecular imaging.

Eligibility:
1. Candidate must have an MD or PhD degree
2. Candidate must be a US citizen, or a non-citizen national, or must have been lawfully admitted for permanent residence and possess an Alien Registration Card (1-151 or 1-551) or some other verification of legal admission as a permanent resident.

Application Deadline: August 1, 2022

APPLY HERE

SMIS WEB SITE

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Fellowships & Scholarships

POSTDOCTORAL FELLOWSHIP : STANFORD CANCER IMAGING TRAINING (SCIT) PROGRAM

Program Directors: Sandy Napel, PhD and Bruce Daniel, MD

The Stanford Cancer Imaging Training (SCIT) Program, funded by the National Cancer Institute, aims to train the next generation of researchers in the development and clinical translation of advanced techniques for cancer imaging and its application. This T32 training program is the evolution of the longstanding program, formerly known as “Advanced Techniques for Cancer Imaging and Detection,” established and led by former Radiology Chair, Dr. Gary M. Glazer in 1992.

SCIT is a two-year program training five fellows (roughly half PhD / half MD) per year over a five-year funding cycle. Drs. Sandy Napel and Bruce Daniel lead the program, featuring mentors with independent cancer-focused or -related funding, and several distinguished program advisors. The required coursework component includes two courses in the clinical/cancer sciences, two in imaging science, one in biostatistics, one in medical ethics (“Responsible Conduct of Research”), and attendance at a minimum of six multidisciplinary tumor boards. In addition, trainees can select from a multitude of electives offered by various Stanford University Departments, e.g., Radiology, Radiation Oncology, Bioengineering, Biomedical Informatics, and Cancer Systems Biology. The primary focus of the program is participation in a mentored cancer-imaging research project aimed at publication in peer-reviewed journals, and presentation at national meetings. Residency-trained radiologists would receive 6 months of clinical training during a two year training period. The program especially features “paired mentorship,” in which each trainee is teamed with both a basic-science and physician mentor, to provide guidance in course and research-topic selection, and in performing clinically-relevant cancer imaging research.

Eligibility:

1. Candidate must have an MD or PhD degree. If candidate has completed a radiology residency, she or he will receive 6 months of clinical training during the 2 year award period.

2. Candidate must be a US citizen, or a non-citizen national, or must have been lawfully admitted for permanent residence and possess an Alien Registration Card (1-151 or 1-551) or some other verification of legal admission as a permanent resident. 

Application Deadline: July 1, 2022

APPLY HERE

SCIT WEB SITE

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Workshops, Seminars, & Events

 NextProf Pathfinder applications are now open!

The University of California San Diego and the University of Michigan are pleased to announce the 2022 NextProf Pathfinder Future Faculty Workshop on October 2-4, hosted in La Jolla, California.

This workshop is designed for 1st and 2nd year Ph.D. students and master’s students intending to apply to a Ph.D. program. NextProf Pathfinder prepares participants for a successful career in academia over three days by offering information from current faculty on what it takes to build a competitive graduate school record to obtain a faculty position in the professoriate.

NextProf Pathfinder encourages those in groups underrepresented in the engineering and computer science professoriate to pursue careers in academia. It is open to U.S. citizens and permanent residents of any ethnicity, race, sexual orientation, gender identity, age, ability, veteran status, socio-economic status, first generation to college status, and religion. All travel and housing costs are covered for accepted applicants.

Applications are now open on https://nextprofpathfinder.engin.umich.edu/, and will close on June 22nd.

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Announcements Defenses

UPCOMING DISSERTATION DEFENSE – VARUN KHEMANI

Name: Varun Khemani
Title: Prognostics and Secure Health Management of Analog Circuits
Committee Members:
Professor Michael G. Pecht, Chair
Dr. Michael H. Azarian, Co-Chair
Professor Peter Sandborn
Professor Abhijit Dasgupta
Professor Mark Fuge
Professor Pamela Abshire, Dean’s Representative

Date: Friday, May 13th, 2022 Time: 01:00 PM Eastern Time (US and Canada)Location: EGR-2164 (ENGR)
Zoom Link:https://umd.zoom.us/j/96565970442?pwd=MU1xMWJyZUJEbS9zS1Zla21EN3hLdz09
Meeting ID: 965 6597 0442
Passcode: 4VrtYb

Abstract:
Analog circuits are a critical part of industrial circuits and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit prognosis and health management (PHM) is critical to the health of industrial circuits. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with the number of circuit components. Therefore, this research presents an advanced design of experiments-based (DOE) approach to account for components that degrade in an individual and interacting fashion, to narrow down the number of possible fault classes under consideration. A wavelet-based deep-learning approach is developed that can localize the circuit component that is the source of degradation and predict the exact value of the degraded component. This degraded value is used in conjunction with physics-of-failure models to predict when the circuit will fail based on the source of degradation.
Increasing outsourcing in the fabrication of electronic circuits has made them susceptible to the insertion of hardware trojans by untrusted foundries. In many cases, hardware trojans are more destructive than software trojans as they cannot be remedied by a software patch and are impossible to repair. Process reliability trojans are a new class of hardware trojans that are inserted through modification of fabrication parameters and accelerate the aging of circuit components. They are challenging to detect through traditional trojan detection methods as they have zero area footprint i.e., require no insertion of additional circuitry. The PHM approach is modified to detect these hardware trojans in order to incorporate circuit security, resulting in the PSHM framework.
Deep neural networks achieve state-of-the-art performance on classification and regression applications but are a black-box approach, which is a concern for implementation. Wavelets are approximations of cells found in the human visual cortex and cochlea. They were used to develop wavelet scattering networks (WSNs), which were intended to be an interpretable alternative to deep neural networks. WSNs achieve state-of-the-art performance on low to moderately complex datasets but are inferior to deep neural networks for extremely complex datasets. Improvements are made to WSNs to overcome their shortcomings in terms of performance and learnability. Further applications of the research are highlighted for rotating machinery vibration analytics, functional safety online estimation. 

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Announcements Workshops, Seminars, & Events

2022 Spring MeGrad Award Ceremony

Thank you to all of those who attended our Annual Spring Award Ceremony and congratulations again to all of our awardees on your many achievements and successes! You can explore photos from the event by clicking here.

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Defenses Uncategorized

UPCOMING DISSERTATION DEFENSE – JOSEPH BAKER

Name: Joseph Baker

Title: ANALYSIS OF MASS TRANSFER IN ELECTROCHEMICAL MEMBRANE PUMPING DEVICES

Committee Members:

Professor Reinhard Radermacher, Chair
Research Professor Yunho Hwang, Co-Chair
Professor Chunsheng Wang
Associate Professor Katrina Groth
Professor Bao Yang
Professor Peter Sunderland, Dean’s Representative

Date: Thursday, May 12th, 2022
Time: 2:00PM
Location: CEEE conference room, 088-4164B
Zoom Link: https://umd.zoom.us/j/2448374295

Abstract:

Considering the environmental challenges posed by traditional energy systems, we must strive to
seek out innovative strategies to sustainably meet today’s demands for energy and quality of life.
Energy systems using electrochemical (EC) energy conversion methods may help us to transition
to a more sustainable energy future by providing intermittent renewable energy storage and
improving building energy efficiency. EC pumping devices are a novel technology that use
chemical reactions to pump, compress, or separate a given working fluid. These devices operate
without any moving parts. Unlike mechanical pumps and compressors, they operate silently,
producing no vibrations and requiring no lubrication. In this dissertation, I examine the
applicability for EC pumping devices in energy storage via compressed ammonia and in
dehumidification for air conditioning.

Hydrogen fuel cells are a promising technology for on-demand renewable power generation.
While storage of pure hydrogen fuel remains a problem, ammonia is an excellent hydrogen
carrier with far less demanding storage requirements. EC ammonia compression opens the door
to several possibilities for separating, compressing, and storing ammonia for intermittent power
generation. Using the same proton exchange membranes commonly used in fuel cells, I
demonstrated successful ammonia compression under a variety of operating conditions. I
examined the performance of a small-scale ammonia EC compressor, measuring the compression
and separation performance. I also conducted experiments to investigate the steady-state
performance of a multi-cell ammonia EC compressor stack, observing a maximum isothermal
efficiency of 40% while compressing from 175 kPa to 1,000 kPa. However, back diffusion of
ammonia reduced the amount of effluent ammonia by as much as 67%.

Dehumidification represents a significant portion of air conditioning energy requirements.
Separate sensible and latent cooling using EC separation of water may provide an energy
efficient thermal comfort solution for the hot and humid parts of the world. I conducted
experiments of several EC dehumidifier, considering both proton exchange and anion exchange
processes. Diffusion of the working fluid was significant in this application as well. I observed a
maximum Faradaic efficiency for dehumidification of 40% for a 50 cm2 cell using an anion
exchange membrane under the most favorable case. I developed a novel open-air EC
dehumidifier prototype. To alleviate the back diffusion issue, I investigated a method for mass
transfer enhancement using high-voltage fields. I also developed a numerical model to simulate
the performance of the EC dehumidifier devices, predicting the experimentally measured
performance to within 25%.

Categories
Fellowships & Scholarships

2022 ExxonMobil LOFT Fellowship Program

ExxonMobil LOFT Fellowship

ExxonMobil Corporation, the largest publicly traded international energy company, has partnered with the Hispanic Heritage Foundation (HHF) to create fellowship opportunities in the STEM fields (Science, Technology, Engineering, and Math) for Hispanic college students. Through HHF’s Latinos On Fast Track (LOFT) program, ExxonMobil seeks to nurture motivated college students across the country, with a passion for Engineering and Science.

Selected students will be exposed to a top-quality company by participating in this signature mentoring program. Each student will be paired up with an ExxonMobil professional to learn how their knowledge and skills are applied in a corporate setting. 

Fellowship Components
The Fellowship consists of five 1-hour meetings with a mentor (virtual or in person), participation in the Harvard-certified ExxonMobil mentee program (on-line), an exclusive curriculum to introduce Fellows to ExxonMobil and career opportunities, and a $1,000 educational grant. In addition, there exists the possibility to interview for ExxonMobil positions (internships/full-time) upon successful completion of the Fellowship.

Fellowship Requirements
This opportunity is highly competitive and open to rising sophomores, juniors, Seniors, and Graduate Students attending a 4-year college or university.
Fellowship applicants with the following majors will be given priority: 

·       Civil Engineering

·       Chemical Engineering 

·       Computer Engineering

·       Electrical Engineering

·       Geoscience 

·       Material Science and Engineering 

·       Mechanical Engineering


Applicants with these majors will be considered also: Chemistry, Computer Science Environmental Engineering, Industrial Engineering, Mathematics, Petroleum Engineering, and Physics.

Other Requirements:

·       Must plan to pursue a career in the fields/majors listed above

·       Have a GPA of 3.5 or higher

·       Be a U.S. citizen or legal permanent resident of the United States with a valid Social Security Number at the time of application. All legal permanent residents must submit a copy of their valid permanent resident card or passport stamped I-551 (not expired).

 
For the 2022 ExxonMobil LOFT Fellowship

If you have questions please contactJulian@hispanicheritage.org. Please use “ExxonMobil LOFT Fellowship” in the subject line.

The 2022 ExxonMobil LOFT Fellowship Application will close May 15, 2022, @11:59 p.m EDT.


This opportunity available through the LOFT program are for Hispanic students.

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Workshops, Seminars, & Events

Trailblazers in Engineering Program

The deadline for receiving applications from TBE (Black/ Latinx Trailblazers) is June 2nd and the actual program will be held in-person on Purdue campus on July 26-28th, 2022. Apply here: https://engineering.purdue.edu/Engr/Trailblazers

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Defenses

UPCOMING DISSERTATION DEFENSE – LEI GAO

Name: Lei Gao

Title: Optimum Design and Operation of Combined Cooling Heating and Power (CCHP) System With Uncertainty

Committee Members:
Prof. Reinhard Radermacher (Chair/Advisor)
Prof. Mark Fuge
Prof. Steven Gabriel
Prof. Jelena Srebric
Prof. Peter Sunderland, Dean’s Rep
Prof. Yunho Hwang
Prof. Vikrant Aute

Date: Monday, May 2nd, 2022
Time: 9:00 AM
Location: EGR-4164B (ENGR)
Zoom link: https://umd.zoom.us/j/6085437805?pwd=NlRsYWIwSHczR1k4bkxaUnZENkJmQT09 (ID: 6085437805, passcode: 926173)

Abstract:
Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems.

As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%.

Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system’s configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art.

Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 $/m2, and a potential annual cost is about 22.33 $/m2.

This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system.

Categories
Fellowships & Scholarships

AWRA Diversity, Equity, and Inclusion Scholarship

The American Water Resources Association (AWRA) has started our first inaugural Diversity, Equity, and Inclusion Scholarship which includes one-year association membership and complimentary conference registration for each of up to three scholarship winners from under-represented groups in the water resources field.

AWRA prides itself on providing multi-disciplinary community, conversation, and connection in the field of water resources. We are committed to diversity, equity, and inclusion in our membership and programs and realize that there are systemic barriers to entrance in the water resources field.

To help address and remove these barriers, AWRA’s Board of Directors has established a Diversity, Equity, and Inclusion Scholarship. It is our intent to continue to offer this scholarship each year.

The Goal: Bring diverse perspectives and support the career advancement of under-represented professionals in the field of water resources.

The Benefits: Each year, up to three scholarship winners will each receive one-year of AWRA professional membership and one free conference registration of their choice. In addition, scholarship winners will be matched with a mentor from among AWRA’s leadership ranks, such as a board member, committee chair, or state section chair.

Membership benefits include a subscription to Water Resources IMPACT magazine, a subscription to the Journal of the American Water Resources Association (JAWRA), free registration for webinars, conferences, membership and leadership opportunities in AWRA’s committees, as well as access to the career center and membership directory. Applications are due May 2, 2022. Click here to learn more about the award criteria and how to submit an application!