Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- SciLifeLab
- Chalmers University of Technology
- Umeå University
- University of Lund
- KTH Royal Institute of Technology
- Blekinge Institute of Technology
- Uppsala universitet
- Jönköping University
- Luleå University of Technology
- Linköping University
- Lulea University of Technology
- Lunds universitet
- University of Skövde
- Helmholtz-Zentrum München
- Karlstad University
- Karolinska Institutet (KI)
- Karolinska Institutet, doctoral positions
- Linköping University (LiU)
- Linnaeus University
- Swedish University of Agricultural Sciences
- The Swedish University of Agricultural Sciences
- The University of Skövde
- Umeå universitet
- Uppsala University
- universitypositions
- 15 more »
- « less
-
Field
-
domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
-
Machines and Power Electronics, and (2) Power Grid and Components. In research unit Electrical Machines and Power Electronics, we are about 25 persons, of which two third are PhD students. Our research
-
Electrical Machines and Power Electronics, we are about 25 persons, of which two third are PhD students. Our research directions include: (1) electrical machines; (2) traction inverters; (3) high frequency
-
Artificial Intelligence and Machine Learning development, Proficiency in written and oral communication in English. Place of employment: Karlskrona. Employment level: 100%. Commencement: Early Fall 2026, exact
-
You must have: a PhD in microbiology, infection biology, or closely related relevant fields. Preference will be given to applicants who have completed their PhD or attained equivalent expertise
-
, education, collaborations, or entirely new initiatives, you will find colleagues who are willing to support you and help make things happen. QUALIFICATIONS You should have a PhD in the relevant area and
-
your application: Experience in deep machine learning, finite element modelling, biomechanics, and anatomy What you will do Take courses at an advanced level within the Graduate school of Machine and
-
: Onboarding and Integration, and Innovation and Creative Capacity. The work will focus on investigating how hybrid work arrangements influence learning, collaboration, innovation, and organizational performance
-
qualifications: Coursework or thesis work in complex systems, network science, agent-based modelling, transport modelling, urban analytics, resilience, computational social science, data science, machine learning
-
research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related