Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
First Stage Researcher (R1) Positions PhD Positions Application Deadline 5 May 2026 - 23:59 (Europe/Stockholm) Country Sweden Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer
-
conduct world-class applied research. We change and make a difference. Do you want to become one of us? Work description The PhD positions are part of the research portfolio within Mechanical Engineering
-
future research projects in which research questions, empirical studies, and method development are shaped by real-world challenges across different application domains. The research focuses is exploring
-
engaging in ongoing and future research projects in which research questions, empirical studies, and method development are shaped by real-world challenges across different application domains. The research
-
Researcher (R1) Positions PhD Positions Application Deadline 18 May 2026 - 23:59 (Europe/Stockholm) Country Sweden Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Dec
-
conduct world-class applied research. We change and make a difference. Do you want to become one of us? The PhD position is located within Department of Computer Science (DIDA) SDS (secure and distributed
-
that work individually or in swarms (groups). The focus is on developing methods within control, sensor and communication systems, for such systems. The work involves both simulation and practical testing
-
development processes Exploring techniques for improving the reliability and trustworthiness of AI-assisted software changes The work combines methods from software engineering, data analysis, and AI, and
-
and RAG modules challenges conventional architectural styles, architecture evaluation methods, and governance models, requiring new approaches for modularity, decoupling, versioning, deployment, and
-
hired at BTH will mainly focus on two aspects of neuromorphic computing: Guidelines / frameworks for mapping applications to neuromorphic systems. Efficient training methods of neuromorphic applications