874 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions in Sweden
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- University of Lund
- Lunds universitet
- Umeå University
- Swedish University of Agricultural Sciences
- Linköping University
- SciLifeLab
- Lulea University of Technology
- Karolinska Institutet (KI)
- Nature Careers
- KTH Royal Institute of Technology
- Umeå universitet
- Uppsala universitet
- Jönköping University
- Stockholms universitet
- Umeå universitet stipendiemodul
- University of Borås
- Mälardalen University
- Örebro University
- Blekinge Institute of Technology
- Karlstad University
- Sveriges Lantbruksuniversitet
- Luleå University of Technology
- Luleå tekniska universitet
- Linnaeus University
- University of Gothenburg
- KTH
- Karolinska Institutet, doctoral positions
- Karlstads universitet
- Lund University
- Luleå university of technology
- Malmö university
- Göteborgs Universitet
- Linköpings universitet
- Linneuniversitetet
- Academic Europe
- Högskolan Väst
- IFM, Linköping University
- Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
- Linnéuniversitetet
- SLU
- Stockholm University
- The Faculty of Education and Society
- Umea University
- Department of Forest Genetics and Plant Physiology
- European Magnetism Association EMA
- Faculty of Culture and Society
- Faculty of Health and Society
- Faculty of culture and society
- Göteborg Universitet
- Göteborgs universitet, Department of Marine Sciences
- Higher Education Institute
- IFM/Linköping University
- Institute of Medicine, Sahlgrenska Academy, University of Gothenburg
- Kungliga Tekniska högskolan
- LInköpings universitet
- Lule university of technology
- Luleå
- Sveriges Lantrbuksuniversitet
- The Faculty of Health and Society
- The Faculty of Odontology
- The Faculty of Technology and Society
- The Swedish University of Agricultural Sciences (SLU)
- Umeå Plant Science Center
- University of Gothenburg, Department of Education, Communication and Learning
- University of Skövde
- 56 more »
- « less
-
Field
-
a strong constellation of both traditional applied biostatistics and expertise in artificial intelligence and machine learning, which is undergoing rapid development. The clinical activities
-
projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
-
–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
-
will be found on our career site: https://www.oru.se/english/career/available-positions/applicants-and-external-experts/ The application deadline is1st of April, 2026. We look forward to receiving your
-
accordance with Karolinska Institutet’s template (http://ki.se/qualificationsportfolio) . You may change or add to your application at any time up to and including the application deadline date. After
-
://www.academiceurope.com/ads/software-engineer-w-m-d-fur-batteriespeicher-analyse/ Do not overlook the inclusion of Academic Europe in your application. Where to apply Website https://www.academiceurope.com/ads
-
are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH Type of employment: Temporary
-
background. However, for this project you must also be open to learn to include social science perspectives on the energy transition by means of cooperation with other research groups. Who we are looking
-
to contribute to a positive work environment. We also value the ability to work independently in carrying out work tasks, as well as openness to learning new skills and taking on new responsibilities. We value
-
highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with