Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- National University of Singapore
- University of Oslo
- Nanyang Technological University
- Zintellect
- University of Stavanger
- University of Bergen
- Center for Drug Evaluation and Research (CDER)
- Harvard University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- UiT The Arctic University of Norway
- University of British Columbia
- University of California
- University of Texas at Austin
- Humboldt-Universität zu Berlin
- INESC TEC
- NTNU - Norwegian University of Science and Technology
- University of North Carolina at Charlotte
- University of South-Eastern Norway
- City of Hope
- Cornell University
- DAAD
- Dana-Farber Cancer Institute (DFCI)
- FEUP
- Indiana University
- LINGNAN UNIVERSITY
- Lawrence Berkeley National Laboratory
- Northeastern University
- Oden Institute for Computational Engineering and Sciences
- Oslo University Hospital
- Queen's University Belfast
- Universidade Católica Portuguesa - Porto
- Universidade de Coimbra
- Universidade do Minho
- University of Agder
- University of Algarve
- University of Aveiro
- University of Inland Norway
- University of London
- Virginia Tech
- 29 more »
- « less
-
Field
-
1. A PhD degree in a relevant area, e.g., Chemical Engineering, Control Engineering, Computer Science, Systems Engineering, etc. 2. The candidates should have strong math, modeling, programming, and
-
or reinforcement learning o Sensor fusion and state estimation o Motion planning and control • Excellent programming skills in Python and/or C++. • Experience with ROS
-
design in real-time simulation environment (Typhoon HIL) and conduct real time simulation studies/experimentation. • Design and develop an intelligent and optimal switching strategies, control
-
modeling results to establish robust process–material–structure relationships and support informed optimization strategies. Support automation, control, and in-process monitoring development for a novel
-
the kinematics, propulsion efficiency, trajectory control, and overall system performance of soft crawling robots. • Development and optimization of control strategies for electronics-free fluidic logic and
-
Description They will lead the development of scalable catalyst manufacturing processes by translating laboratory formulations into kilogram-scale production, optimizing catalyst composition, support materials
-
processes, stochastic control, optimization, or reinforcement learning; • Solid mathematical training and ability to work with rigorous proofs; • Familiarity with Markov decision processes, dynamic
-
, completion-based, and operational flow control strategies to optimize geothermal energy production performance. Research will combine reservoir simulation, field data analysis, and coupled modeling
-
materials, the design and construction of optoelectronic devices, as well as characterization and testing. The main objectives include: 1. Controllable synthesis of high-quality 2D materials and
-
inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security