Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
literature, articulate targeted problem formulations, select appropriate methodology and methods, collect data, and systematically and critically analyze the findings from your studies. During this PhD journey
-
these systems operate in, ACPS increasingly rely on data-driven learning-enabled components to perform a variety of challenging decision-making tasks. While indispensable for autonomy, learning-enabled components
-
computer. The WACQT team at Chalmers currently has about 100 members (faculty, permanent research staff, postdoctoral researchers, PhD students, and undergraduate students). WACQT is committed to promoting
-
. Strategic management of platforms and innovation ecosystems – open innovation, platform orchestration, and multi-stakeholder partnerships in dynamic environments Strategic use of intellectual assets and data
-
Sensing Division to advance research in remote sensing instrumentation or information retrieval, and to educate the next generation of engineers and scientists. About us The GEO division has a strong track
-
collaboration with the Multiscale Inorganic Materials group, both part of the Division of Energy and Materials at Chalmers . The two groups together comprise nine senior researchers and 27 PhD students and
-
aimed at building a high-performance quantum computer based on superconducting circuits. Our team includes a dynamic mix of PhD students, postdocs, and senior researchers working collaboratively
-
universities around the world. The department provides a friendly, creative, and supportive atmosphere with a steady flow of international guests. More information about us can be found on our website: http
-
within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences
-
digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge