Sort by
Refine Your Search
-
Category
-
Country
-
Employer
-
Field
-
learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
-
heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
-
numerical simulations to reproduce and predict magnetically confined fusion plasma experiments 2.Development of transport models based on simulation data and their implementation into integrated transport
-
Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
-
problems. Responsibilities may include: Assisting with clinical data analysis and preprocessing Developing and evaluating AI and machine learning models Supporting research in predictive modeling and
-
models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very
-
Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
-
Researcher for the Mechanical Systems Modeling (MSM) Group. The Electric Motor Researcher will conduct detailed analysis of electric motors and motor drive systems used in gas centrifuge applications
-
, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
-
the company ONCONTROL, in cooperation with the UC. The fellowship will be involved in the execution of the following main objectives: - Optimization of the process digital twin - Development of a predictive