Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design
-
of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
-
the development of system-level digital-twin and optimization methodologies—integrating electrolyzer models with renewable generation, power electronic conversion, energy and hydrogen storage, downstream processes
-
engineering parts. This position offers a unique opportunity to drive the development of system-level digital-twin and optimization methodologies—integrating electrolyzer models with renewable generation, power
-
to build and run advanced numerical models, carry out simulations and analyse results in a systematic and transparent way. Depending on ongoing projects, you may be involved in collaboration with industrial
-
research profile is clearly connected to thermal Modelling, and you can demonstrate experience with CFD modelling. You have solid skills in modelling and simulation using Tools and data processing and
-
dynamic models for simulation, analysis and control design, and you are motivated by developing and implementing model-based control solutions that must perform reliably in a real pilot-scale system. You
-
nitrogen dynamics, and climate change mitigation potentials in agroecosystems. You will be contributing specifically to the area of regional simulation using process-based models and advanced statistical
-
. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness
-
. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust