Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Blekinge Institute of Technology
- KTH Royal Institute of Technology
- University of Lund
- Lunds universitet
- Uppsala universitet
- Linköping University
- SciLifeLab
- Swedish University of Agricultural Sciences
- Umeå University
- Örebro University
- European Magnetism Association EMA
- Göteborgs universitet
- Helmholtz-Zentrum München
- Karlstads universitet
- The University of Skövde
- University of Skövde
- Uppsala University
- 8 more »
- « less
-
Field
-
is BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and
-
architecture, parallel systems, programming language theory, embedded systems or software engineering. Documented ability to teach operating systems and compiler construction. Practical experience of systems
-
sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and master’s levels, as well as the programmes in
-
through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
-
contribute to the development of teaching and learning within the subject area. The research can be based on controlled experiments as well as production data from commercial farms, questionnaire-based studies
-
addition to conventional software, the scope includes engineering of AI enabled systems (primarily ML and LLM), and thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI
-
new methods for integrated sensing and communications in optical networks. Cutting-edge machine learning techniques for sensing data analysis, models of the impact of external phenomena on optical
-
. The successful candidate will also contribute to the development of teaching and learning within the subject area. The research can be based on controlled experiments as well as production data from commercial
-
, multi-omics data integration using machine learning, and potential collaborations with clinical and translational researchers. The project is well-suited for candidates with a background in bioinformatics
-
Learning has an open position for a doctoral student with a background and strong interest in deep generative learning and computer vision/remote sensing. The successful candidate will join a project funded