65 parallel-computing-numerical-methods positions at Chalmers University of Technology
Sort by
Refine Your Search
- 
                Listed
- 
                Category
- 
                Program
- 
                Field
- 
                
                
                , critical in the design of plasma confinement devices. Modus operandi in the project is to make progress on these difficult question via structure-preserving numerical methods (cf. "matrix hydrodynamics 
- 
                
                
                is, however, the transportation and storage. Current methods rely on liquid compressed hydrogen, which requires high pressures or low temperatures. This project will computationally explore catalyst 
- 
                
                
                , Biomedical Engineering, Applied Mechanics, or a closely related field (awarded no more than three years prior to the application deadline)* Strong background in computational mechanics and numerical methods 
- 
                
                
                Optimal Control Theory Strong programming skills in C++/Python/MATLAB Familiarity with parallelization and high performance computing (CPU and GPU friendly code) Experience with Machine Learning, generative 
- 
                
                
                staff position within a Research Infrastructure? No Offer Description Join our computational mechanics team at Chalmers University of Technology ! As a Doctoral student with us, you will develop numerical 
- 
                
                
                We invite applications for several PhD positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment of the Wallenberg Centre 
- 
                
                
                We invite applications for several postdoctoral research positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment 
- 
                
                
                the lab, you will be exposed to a broad range of computational methodologies, ranging from material characterization, via machine-learning and high-throughput methods, to ab initio calculation 
- 
                
                
                actions to evaluate, balancing safety and computational effort. You will compare deep learning–based methods and probabilistic machine learning approaches, and explore extensions to active reachability 
- 
                
                
                applying methods from quantum field theory, computational physics, statistics, and applied mathematics. Within astroparticle physics, our focus spans from the theoretical modeling of systems and phenomena