Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Technical University of Denmark
- Cranfield University
- Forschungszentrum Jülich
- NTNU - Norwegian University of Science and Technology
- Utrecht University
- ; The University of Manchester
- Chalmers University of Technology
- Curtin University
- Monash University
- University of Groningen
- University of Twente
- University of Twente (UT); Enschede
- Wageningen University and Research Center
- ; Brunel University London
- ; St George's, University of London
- AALTO UNIVERSITY
- Arts et Métiers Institute of Technology (ENSAM)
- DAAD
- Erasmus University Rotterdam
- Ghent University
- Harper Adams University
- ICN2
- Imperial College London
- KNAW
- King's College London
- Leibniz
- Nature Careers
- Norwegian Meteorological Institute
- SINTEF
- SciLifeLab
- Technical University of Denmark;
- University of Adelaide
- University of Birmingham
- University of Nottingham
- University of Sheffield
- University of Southern Denmark
- University of Twente (UT)
- University of Warwick
- Uppsala universitet
- Vrije Universiteit Brussel
- 30 more »
- « less
-
Field
-
control algorithms lies a physics-based simulation model, whose accuracy largely determines the effectiveness of the control loop. Position 3 – High-fidelity simulation of the LAFP process Current
-
handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during forming. With
-
to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients’ recovery progress, timely detect and even predict clinical adverse events like delirium
-
the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
-
/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
-
and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
to these various dangers, to inform the design of, and test, mathematical models that will be generally applicable across a larger cross-section of important species of bacteria. Modelling Evolution – Predicting
-
model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
-
marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong