Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
Your profile PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science with a strong theoretical focus. Candidates should have an exceptional academic record and a robust mathematical foundation. While research experience is advantageous for PhD...
-
Sprachauswahl FAQ de Stellenportal chevron_left Übersicht Partnerinserat chevron_left Übersicht Research Associate – AI-Driven Process Optimization for Smart Manufacturing 100%, Zurich, fixed-term
-
method (FEM) simulations using metamodeling techniques and Machine Learning (ML). By enriching datasets and leveraging advanced simulations to optimize ML models, we seek to enhance manufacturing
-
Your profile Candidates should have an exceptional academic record and a robust mathematical foundation. They should have published works at the main conferences in the field of machine learning, such as ICML, NeurIPS, ICLR, etc. Excellent communication skills and fluency in English (spoken and...
-
Doctoral (PhD) Student Positions in Control and Optimization for 3D Printing The Automatic Control Laboratory (IfA) in the Department of Information Technology and Electrical Engineering of ETH Zurich is a
-
Intelligence in Mechanics and Manufacturing (AIMM) at ETH Zurich, is offering a position in the field of data-driven optimization. Project background Our research focuses on the development and application
-
-relevant research challenges such as controlling high precision production machines, optimizing machine tools, or conceiving sustainable manufacturing processes. This position is envisaged to be part of
-
) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
-
also recognizes the unequal capacities and risks involved, highlighting the “cruel” ascriptions to optimism and the potentially devastating consequences of aspiration. ASPIRA's approach identifying
-
) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software