Sort by
Refine Your Search
-
We are seeking a PhD candidate to join our team in the development of next-generation bioelectronic platforms to study implant interfaces and modulation of the brain tissue microenvironment
-
-wide failures. The goal of this project is to develop a theoretical foundation for understanding and mitigating memory poisoning in LLM agents. This position, funded by the Swedish Research Council (VR
-
. Your work assignments The overall aim of the project is to investigate how firms can leverage specialised AI models to develop innovative offerings, business models, and ecosystem collaborations. While
-
of real-time behavior, security breaches, and system-wide failures. The goal of this project is to develop a theoretical foundation for understanding and mitigating memory poisoning in LLM agents
-
cybersecurity lab, vulnerabilities in isolated systems are analyzed to pioneer new research methods and train both students and professionals. More about cybersecurity research at LiU: https://liu.se/en/research
-
equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
-
solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
-
positions in the interdisciplinary research environment “Culture and Society” to develop research on themes relevant to the thematic area “Digital Society, Infrastructure, Legacies”. This thematic area
-
usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
-
application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning