Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
OPTIMIZATION FOR COMPLEX ENERGY AND SUSTAINABILITY PROCESSES Are you looking for a future industrial research position or a long-term academic career? Do you want to be part of building up research – preferably
-
School of Engineering invites applications for Doctoral Researcher in Sustainable Renewable Energy Engineering, Modeling and Optimization Department of Energy and Mechanical Engineering is a community of
-
into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design
-
into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design
-
, fabrication, and testing of functional thin film coatings. Key responsibilities include: Developing and optimizing advanced surfaces using state-of-the-art thin film technologies Characterizing prepared thin
-
to advanced control design and system optimization. Our specialty is developing embedded control, estimation, and identification algorithms that directly interface with physical hardware. We work closely with
-
. 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
-
advanced and sustainable manufacturing, integrated in the circular economy. The starting point in modern production at M2P is digital data, generated and optimized. Research methods encompass multiscale and
-
. 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
-
systems. We are now looking for a Doctoral Researcher. Job Description Project title: Offline learning for adaptable control of a multi-actuator boom using existing large but sub-optimal datasets