Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Purdue University
- ;
- Cornell University
- AALTO UNIVERSITY
- Duke University
- Imperial College London
- Susquehanna International Group
- University of Newcastle
- Brookhaven Lab
- SUNY
- Texas A&M University
- The University of Iowa
- University of Cambridge
- University of Houston Central Campus
- University of Louisville
- University of Minnesota
- University of Minnesota Twin Cities
- University of New Hampshire – Main Campus
- University of North Carolina at Chapel Hill
- University of Southern California
- University of Tennessee at Chattanooga
- University of Utah
- Virginia Tech
- 13 more »
- « less
-
Field
-
techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
-
The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
-
December 2018, is to improve patient outcomes by providing education on safe and effective controlled substance prescription use and to serve as a catalyst for collaborative research efforts optimizing
-
. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
-
Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
-
expertise in wireless communications, communication theory, information theory, applied probability, and optimization • Excellent written and verbal communication skills Preferred Qualifications • Prior
-
Decision Intelligence for Supply Chain and Operations Optimization. The successful candidate will contribute to cutting-edge research at the intersection of Statistical Machine Learning and Generative
-
simulations to analyze and optimize hemodynamic parameters in CAD-related applications. Collaborate with clinicians to translate simulation insights into practical solutions for cardiovascular health. Develop
-
, determining optimal ways for groups of buildings to share resources and benefits. You will investigate and quantify trade-offs between individual objectives and collective outcomes, focusing on scalability
-
with good understanding of probability, statistics and optimization. * Proven expertise in the implementation and testing of algorithms. * Strong programming skills in R or Python. * Familiarity with