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theory and applications, instrumentation and signal processing and cloud microphysics and dynamics. Strong programming abilities (like Python), statistical knowledge, machine learning expertise, and data
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Chalmers provides a cultivating and inspiring working environment in the coastal city of Gothenburg . Read more about working at Chalmers and our benefits for employees. Chalmers aims to actively improve
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electromagnetic processing. The work involves close cooperation across several departments. You will have access to a well-equipped experimental environment and be part of a collaborative team dedicated to creating
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backgrounds in: Civil, Architectural, and Environmental Engineering, Computer and Software Engineering, Computer Science, or a related discipline such as Mechanical Engineering. Candidates must have completed a
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for analyzing complex ecosystem and environmental datasets. Deep understanding of marine ecosystem processes, particularly within coastal regions. Understanding of underwater light field and its modulating
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focuses on the development of new materials and processes for electronics packaging and bioelectronics applications. The research of the electronics packaging group in the Electronics Materials and Systems
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the coastal city of Gothenburg . Read more about working at Chalmers and our benefits for employees. Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example
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of the Wallenberg Centre for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing
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we offer Chalmers provides a cultivating and inspiring working environment in the coastal city of Gothenburg . Read more about working at Chalmers and our benefits for employees. Chalmers aims
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning