Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
-
overall focus will be to strengthen the department’s expertise in closing the building “performance gap” through data-driven building operation optimization and system innovation. You will bridge advanced
-
perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
-
Build, Division of Civil and Environmental Engineering, within the general study programme Civil Engineering and work with numerical modelling, time series analysis, and soil characterization
-
while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
-
solution for the future of maritime decarbonisation. Your work tasks The research assistant will be involved in high-fidelity numerical modelling and a comprehensive analysis campaign of flexible power
-
to qualify (which is 2 years) Highly skilled in numerical modelling and programming is essential Experience with at least two of the following disciplines is expected: o hydrological and/or land surface models
-
NAS that considers accuracy, latency, energy, and carbon at the same time. Delivering a carbon-aware NAS framework that generates Pareto-optimal model architectures and model libraries whose variants
-
teams with colleagues. Your qualifications You are expected to have a Ph.D. in hydrology and have documented experience with numerical integrated hydrological modelling including groundwater, remote
-
the system to learn optimal adjustment policies directly from interaction with the physical infrastructure. Anomaly detection and fault diagnosis. The candidate will develop a multi-level diagnostic framework