Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Linköping University
- Uppsala universitet
- SciLifeLab
- Umeå University
- Chalmers University of Technology
- Stockholms universitet
- Blekinge Institute of Technology
- Institutionen för molekylära vetenskaper
- KTH Royal Institute of Technology
- Linköpings universitet
- Lulea University of Technology
- Luleå tekniska universitet
- Mid Sweden University
- Nature Careers
- Umeå universitet
- University of Borås
- University of Lund
- 7 more »
- « less
-
Field
-
the subject of Machine Elements, where the research is primarily focused on tribology and its applications. The research group currently consists of about 40 researchers and doctoral students and is one
-
research team, you get the chance to develop in tribology, work interdisciplinary and conduct research in close collaboration with industry. You will be working in the subject of Machine Elements, where
-
Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
-
independently. Merits: Education or training in computer vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics
-
related field and have previous academic experience in machine learning. The candidate should have a strong background in metrology and medical image processing. Active participation and collaboration
-
· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively
-
methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical
-
multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
-
, and registry-linked outcome data. In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen
-
identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an