Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- Linköping University
- KTH Royal Institute of Technology
- Lunds universitet
- Uppsala universitet
- Umeå University
- Karolinska Institutet (KI)
- SciLifeLab
- Umeå universitet stipendiemodul
- Nature Careers
- KTH
- Lulea University of Technology
- Umeå universitet
- University of Borås
- Blekinge Institute of Technology
- Jönköping University
- Karlstad University
- Karlstads universitet
- Swedish University of Agricultural Sciences
- University of Lund
- Göteborgs Universitet
- IFM, Linköping University
- Institutionen för akvatiska resurser
- 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
- Örebro University
- 27 more »
- « less
-
Field
-
support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
-
computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
-
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
-
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
-
highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
-
(e.g., from the viewpoint of physics, chemistry, or mechanical engineering), programming, machine learning, or equipment automation (including microfluidic systems, robotics and remote sensing
-
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
-
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
-
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