12 postdoc-in-thermal-network-of-the-physical-building PhD positions at Linköping University
Sort by
Refine Your Search
-
European Marie Sklodowska-Curie Doctoral Network FADOS. The successful candidate will join a cohort of 17 doctoral students based at 16 research groups in Europe and the UK. About FADOS FADOS, Fundamentals
-
8 Oct 2025 Job Information Organisation/Company Linköping University Research Field Physics Researcher Profile First Stage Researcher (R1) Country Sweden Application Deadline 10 Nov 2025 - 12:00
-
2 Sep 2025 Job Information Organisation/Company Linköping University Research Field Engineering » Materials engineering Chemistry » Applied chemistry Physics » Applied physics Researcher Profile
-
, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en
-
convolutional neural networks by exploring transformers, implicit neural representations (INRs), and hybrid architectures that integrate physical priors such as periodicity, symmetry, and long-range correlations
-
fast-paced research environment, a structured and organized approach is highly valued. You will work in a team of researchers from diverse backgrounds, including PhD students and postdocs, and should
-
student and help build tomorrow’s craft and design heritage. Craft the future! Your work assignments The aim of this doctoral position is to explore how new design languages and aesthetic expressions can
-
on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will work with large-scale behavioural data sets using a range of approaches, including heritability
-
materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
-
-native networks or financial services, AI/ML that is not secure, robust, verifiable, or privacy-preserving can lead to safety risks, regulatory violations, and significant reputational damage. By making AI