12 data-visualization-analysis "https:" Postdoctoral research jobs at Linköping University
Sort by
Refine Your Search
-
. A successful candidate should have very good knowledge in quantitative methodology and related analysis tools, in particular very good knowledge in analysis with registry or survey data from various
-
platforms, security analysis, adversarial AI attacks and defences, intrusion detection, load management in mobile edge networks, or denial-of-service detection are considered as a merit. Earlier experience in
-
Physicochemical and spectroscopic characterization of substrate performance, including sensitivity, reproducibility, stability, and matrix effects Quantitative analysis of spectroscopic data using multivariate
-
of performance and stability in Na- and Zn-based batteries. The work also involves data analysis, method development, and systematic documentation of research results. Furthermore, the postdoctoral researcher is
-
Social Robots, which involves several Swedish universities and is funded by WASP-HS (https://wasp-hs.org/ ). The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS) is a
-
of behavioural biology are required. You should also have solid experience working with large datasets, as well as documented skills in statistical analysis and data visualisation, for example work with regression
-
7 Feb 2026 Job Information Organisation/Company Linköping University Research Field Biological sciences Researcher Profile Established Researcher (R3) Application Deadline 31 Mar 2026 - 12:00 (UTC
-
of novel 2D materials (e.g., thin-film deposition by PVD and CVD). Proven programming skills (e.g., Python) for instrument control and data analysis. You are a highly motivated and independent researcher
-
through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software
-
communication limitations, adversarial conditions, continual and adaptive learning in dynamic environments. The research will combine tools from distributed optimization, stochastic approximation, information