894 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" positions in Sweden
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- Lunds universitet
- Umeå University
- University of Lund
- Swedish University of Agricultural Sciences
- KTH Royal Institute of Technology
- Linköping University
- Uppsala universitet
- Karolinska Institutet (KI)
- SciLifeLab
- Umeå universitet
- Nature Careers
- Lulea University of Technology
- Sveriges Lantbruksuniversitet
- Umeå universitet stipendiemodul
- Stockholms universitet
- Jönköping University
- University of Borås
- Karlstad University
- University of Gothenburg
- Örebro University
- Mälardalen University
- Karlstads universitet
- Karolinska Institutet, doctoral positions
- Linnaeus University
- Luleå University of Technology
- Blekinge Institute of Technology
- KTH
- Luleå tekniska universitet
- Luleå university of technology
- Högskolan Väst
- Linköpings universitet
- Linneuniversitetet
- Academic Europe
- Göteborg Universitet
- Göteborgs Universitet
- IFM, Linköping University
- Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
- SLU
- Sahlgrenska Academy, University of Gothenburg
- Stockholm University
- Umea University
- Department of Forest Genetics and Plant Physiology
- EFSA - European Food Safety Authority
- European Magnetism Association EMA
- Faculty of Culture and Society
- Faculty of culture and society
- Göteborgs universitet, Department of Marine Sciences
- Higher Education Institute
- IFM/Linköping University
- Institute of Medicine, Sahlgrenska Academy, University of Gothenburg
- Institutionen för akvatiska resurser
- Institutionen för växtskyddsbiologi
- Kungliga Tekniska högskolan
- LInköpings universitet
- Linnéuniversitetet
- Lule university of technology
- Luleå
- Luleå tekniska universitet/Luleå University of Technology
- Lund University
- School of Business and Economics, Linnaeus University
- Swedish University of Agricultural Science
- The Institute of Clinical Sciences
- The Swedish University of Agricultural Sciences (SLU)
- The University of Gothenburg
- Umeå Plant Science Center
- University of Gothenburg, Department of Education, Communication and Learning
- University of Skövde
- 58 more »
- « less
-
Field
-
risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
-
). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
-
tools for the design, analysis and evaluation of socially intelligent systems that aim to collaborate with humans in learning and decision-making tasks, often with the aim of improving health. Visit https
-
-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
-
materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
-
world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
-
with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted. The following experience will strengthen your application: Experience in system identification and machine learning is
-
urban, forest, and market data; developing AI-based forecasting and scenario-simulation pipelines that combine machine learning and simulation methods; and creating visual analytics and human-in-the-loop
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
-
assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models