About NREL

At NREL, we focus on creative answers to today’s energy challenges. From breakthroughs in fundamental science to new clean technologies to integrated energy systems that power our lives, NREL researchers are transforming the way the nation and the world use energy.

More on their link: https://www.nrel.gov

“Join us in building a clean energy future. At NREL, you’ll discover how you can make a difference in the world while also growing professionally. We combine our mission with our heart, making NREL a place where you can grow your passion inside and outside the lab.”

More information about opportunities at NREL: https://www.nrel.gov/careers/discover-nrel.html

Active job opportunities (last checked January/2019):

• Postdoctorate – Chemical/Mechanical Engineering for Thermal-Fluid Modeling
• Postdoctoral Researcher – Buildings Materials and Thermal Science
• Post Doctoral Position in Thermo-Mechanical Fatigue Analysis
• “Commercial Buildings Research Engineer – Energy Modeler” (Los Angeles)
• Graduate Intern Position in Solar Collector Optical Characterization using Image-Processing
• Geothermal-Modeling and Simulation Scientist
• Building Research Engineer – Electrical Power Systems
• Researcher – Control and Optimization
• Post-Doctoral Researcher –Development of system engineering tools for wave energy converter applications
• Post-Doctoral Researcher – Structural and Mechanical Engineer in the Water Power Program
• Postdoctoral Researcher – Design and Systems Engineer in the Water Power Program
• Postdoctoral – Integrated Transportation-Energy Systems Modeler

Please join me in congratulating Dr. Phyllis Robinson, Professor of Biological Sciences, for being named as the Robert and Jane Meyerhoff Chair in the Department of Biological Sciences. This three-year appointment honors Dr. Robinson for her many valuable contributions to gender equity issues across UMBC as well as for her exceptional service to the Department, the College of Natural and Mathematical Sciences, and the University. Known for her efforts to increase the participation of women and minorities in science, Dr. Robinson co-founded the university’s Women in Science and Engineering (WISE) group and served as a Co-PI on the UMBC NSF ADVANCE Grant, which is nationally recognized as an exemplary model for institutional transformation programs. She was honored by the UMBC President’s Commission on Women in 2012 and recently received the 2018 Marilyn E. Demorest Faculty Advancement Award for her advocacy work at UMBC.

Dr. Robinson continues to be a dedicated mentor to undergraduate and graduate students as she explores her research interests in vision science, signal transduction, and neurobiology with grant support from the National Institutes of Health (NIH).  She succeeded Dr. Lasse Lindahl as the program director of UMBC’s NIH-funded Maximizing Access to Research Careers Program, a preeminent undergraduate scholars’ program with over 100 alumni who have earned Ph.D. or M.D./Ph.D. degrees. A former Graduate Program Director, Dr. Robinson was recognized for excellence in mentoring by the University System of Maryland Board of Regents in 2002 and by the Leadership Alliance in 2006. Prior to joining the faculty of the Department of Biological Sciences in 1992, the Boston-native did postdoctoral work at Brandeis University, and received a Ph.D. from the University of Wisconsin-Madison and a B.A. in Biology from Wellesley College.

Over the past 26 years, the UMBC community has benefitted from Dr. Robinson’s many gifts that include her passion for research, her talent for mentoring, and her compassion for others. She is eminently qualified for the honor of being a Robert and Jane Meyerhoff Chair. 

“Students Maya Hanna, MPH, and Juan-David Rueda, MD, received the Department of Pharmaceutical Health Services Research’s (PHSR) Student Travel Scholarship to present their research at the 2018 International Society for Pharmacoeconomics and Outcomes Research meeting in Europe. Other PHSR awards to graduate students included the Harris Zuckerman Scholarship to Yoon Hong, PharmD, the Donald O. Fedder Memorial Fellowship to Jacquelyn McRae, PharmD, and the Dr. Arthur Schwartz Memorial Scholarship to Chengchen Zhang, MPH.” (pages 8-9)

Please see page 16 regarding our STAR-PREP Fellows. This is outstanding news!

More about this message from the Presiden in the December 2018 edition at the following link: https://www.umaryland.edu/media/umb/president/the-presidents-message/2018-19/December-2018.pdf

Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The original problem of compressive sensing is NP-hard so the traditional techniques, such as convex and nonconvex relaxations and greedy algorithms, apply stringent constraints on the measurement matrix to indirectly handle this problem in the realm of classical computing.

We propose well-posed approaches for both binary compressive sensing and binary compressive sensing with matrix uncertainty problems that are tractable by quantum annealers. Our approach formulates an Ising model whose ground state represents a sparse solution for the binary compressive sensing problem and then employs an alternating minimization scheme to tackle the binary compressive sensing with matrix uncertainty problem. This setting only requires the solution uniqueness of the considered problem to have a successful recovery process, and therefore the required conditions on the measurement matrix are notably looser. As a proof of concept, we can demonstrate the applicability of the proposed approach on the D-Wave quantum annealers; however, we can adapt our method to employ other modern computing phenomena–like adiabatic quantum computers (in general), CMOS annealers, optical parametric oscillators, and neuromorphic computing.

The post paper: Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty appeared first on UMBC ebiquity.