Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
coordination of signals using cyber-physical modelling theories and internet connectivity integrated into the control of a-grids. However, there will be latency, packet drop-outs in the cyber layer that may
-
motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
-
engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills, including signal processing, optimization, machine learning or information theory; Experience in programming, e.g
-
, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills, including signal processing, optimization, machine learning or information theory; Experience in
-
research agenda. You should be excited about successfully combining theory and practice by developing data-driven methods and perspectives for sensing, computing, and communications engineering, and thrive
-
computing technologies. The group has a long tradition of empirical and solution-oriented research focusing on processes, products, and theory. The PhD fellow will join the Human Augmentation and
-
academic background with a master’s degree or equivalent in Energy Engineering, Electrical Engineering, Mathematics, Control theory, or any other related discipline, potentially with skills in Power
-
degree or equivalent in Energy Engineering, Electrical Engineering, Mathematics, Control theory, or any other related discipline, potentially with skills in Power Electronics Converters and Control
-
theory. The PhD fellow will join the Human Augmentation and Collaboration (HAC) group and the Physical and Embodied Interaction (PEI) group. The HAC group designs and evaluates interactive systems
-
will involve both computational predictions and experimental validation. The project will combine density functional theory calculations with machine learning and molecular dynamics simulations