Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- Linköping University
- Lunds universitet
- KTH Royal Institute of Technology
- Uppsala universitet
- SciLifeLab
- Umeå University
- Karolinska Institutet (KI)
- Nature Careers
- Umeå universitet stipendiemodul
- Umeå universitet
- KTH
- Lulea University of Technology
- University of Borås
- Blekinge Institute of Technology
- Jönköping University
- Karlstads universitet
- Swedish University of Agricultural Sciences
- University of Lund
- Örebro University
- Göteborgs Universitet
- IFM, Linköping University
- IFM/Linköping University
- Institutionen för akvatiska resurser
- Karlstad University
- Karolinska Institutet, doctoral positions
- Kungliga Tekniska högskolan
- LInköpings universitet
- Linköpings universitet
- Linnaeus University
- Linnéuniversitetet
- Lule university of technology
- Luleå university of technology
- SLU
- Stockholms universitet
- Sveriges Lantbruksuniversitet
- Umea University
- University of Skövde
- 28 more »
- « less
-
Field
-
-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
-
-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
-
conducting research "in the wild" (e.g., field deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an
-
(e.g., from the viewpoint of physics, chemistry, or mechanical engineering), programming, machine learning, or equipment automation (including microfluidic systems, robotics and remote sensing
-
data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods
-
to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
-
computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
-
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
-
from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
-
value chains to enable AI-based applications, using methods and models from e.g. operations research, data analytics or artificial intelligence/machine learning. Identify, structure and prioritise