23 condition-monitoring-machine-learning Postdoctoral research jobs at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
-
, computational materials science, computer science, or a related field, awarded no more than three years prior to the application deadline*. Background in physics-based battery modelling and/or machine learning is
-
computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
-
fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
-
-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
-
passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
-
interdisciplinary and to learn new skills and to perform research in collaboration with others. We seek candidates with the following qualifications: A doctoral degree in a Bioscience-related field awarded
-
conducted in a chamber with optical access, where hydrogen-air mixtures of varying homogeneity and flow conditions will be studied at elevated pressures and temperatures. Ignition and flame properties will be
-
commitment to lifelong learning. The department emphasizes strong collaboration between academia, industry, and society, with a clear focus on utilisation. M2 is characterised by an international environment
-
The position's field of research focuses on developing and implementing safe, transparent, and explainable AI systems using multimodal deep learning and Large Language Models (LLMs) for healthcare