Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
, including finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks
-
topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks above, the postdoc can be involved in
-
Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
-
of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in
-
algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
-
environments ensure safe operations Our activities are primarily related to the development and application of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments
-
with GKN Aerospace Sweden with partial work there and combines numerical and experimental research. Project overview The purpose of this project is to contribute to the development of an ultra-efficient
-
division at the department of Electrical engineering at Chalmers. Here, a team of PhD students, post-docs and senior researchers are working on modeling and numerical optimization of problems in the areas
-
Subject description Mathematics in a broad sense, which also includes mathematical statistics, numerical analysis and optimization theory. Duties As an associate professor within the framework
-
, single-cell spectroscopy, multicolour fluorescence and numerical modelling. This multidisciplinary approach will significantly advance our understanding about the resilience of coralline algae to projected