29 algorithm-development-"The-University-of-Edinburgh" positions at Linköping University in Sweden
Sort by
Refine Your Search
-
address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model
-
intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging. Due to limitations in electron dose and scan stability, microscopy
-
mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
-
prominent approach to AI, with impressive performance in many application domains, including materials discovery. This development has a huge potential for societal impact, with applications in renewable
-
, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
-
with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
-
, and includes responsibility for developing the department’s environmental research lab. You will collaborate across disciplines, develop methods aligned with strategic goals, and contribute to seminars
-
skills are integrated in the development of professional and interprofessional identities, and how these are expressed in the daily health care work. Research in medical education also includes interactive
-
application! Work assignments The purpose of the position as an assistant professor is that the teacher should be given the opportunity to develop his independence as a researcher and to merit both
-
at the intersection of AI and advanced electron microscopy. The project focuses on developing novel self-supervised and physics-informed deep learning methods to restore and denoise Transmission