37 approximation-theory-math PhD positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
of Computer Science and Engineering is strongly international, with approximately 300 employees from over 50 countries. The department is a fully integrated department with the University of Gothenburg and Chalmers
-
of adsorption and biodegradation, little is currently known about their interaction. This project aims to fill that knowledge gap by: Operating laboratory-scale GAC filters Sampling from full-scale filters
-
Engineering and Autonomous Systems division . We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and learning
-
research spans microbial ecology theory to process engineering, and includes the development of new biotechnological processes. Examples of previous research areas include: Anammox Fermentation Microbial
-
behavior of programs at a high level. Automata theory — to manipulate logical formulas and domain representations. Two-player games — to reason about strategies and synthesized programs. The work involves
-
Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
-
develop innovative remote sensing capabilities to monitor oceans, ice, vegetation, and natural disasters. Be part of a dynamic, international team shaping the future of environmental monitoring! About us At
-
to operate around the clock. By ensuring the performance, longevity, and circularity of industrial systems such as advanced manufacturing (e.g., automotive and battery) and renewable energy (e.g., energy
-
correctly. Project description The goal of this PhD project is to develop techniques for the design and verification of assured ACPS with a focus on runtime assurance. You will develop theory and tools
-
effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable