Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Chalmers University of Technology
- Lunds universitet
- KTH Royal Institute of Technology
- Uppsala universitet
- Chalmers tekniska högskola
- Umeå University
- University of Lund
- Karolinska Institutet (KI)
- Linköping University
- SciLifeLab
- Umeå universitet stipendiemodul
- Nature Careers
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- Lulea University of Technology
- Umeå universitet
- Luleå tekniska universitet
- Mälardalen University
- University of Gothenburg
- Högskolan Väst
- Linköpings universitet
- Luleå University of Technology
- chalmers tekniska högskola
- Örebro University
- Chalmers
- Chalmers Tekniska Högskola
- Chalmers te
- Chalmers tekniska högskola AB
- Chalmers telniska högskola AB
- Göteborg Universitet
- KTH
- Karlstads universitet
- Karolinska Institutet
- Linköping university
- Linneuniversitetet
- Lund University
- Lund University;
- Mälardalens universitet
- Sveriges Lantrbruksuniversitet
- Sveriges lantbruksuniversitet
- The Royal Institute of Technology (KTH)
- 31 more »
- « less
-
Field
-
disease patterns to underlying geographical and socio-economic factors such as rivers, altitude, trade routes, settlement structures, and rainfall. This step will involve using spatial statistics and space
-
of Mathematical Statistics include stochastic models, statistical theory and computational statistics, probability theory and statistical signal processing, with applications in areas such as financial mathematics
-
treatment. Develop methods and models that can predict the course of the disease by analyzing detailed data on the immune system and metabolism. We are conducting a large study, the CoVUm study, involving 579
-
new research team, including senior researchers and PhD students, combining expertise to advance battery research. You’ll draw on advanced models that assess how charging, temperature, and balancing
-
-series modeling (EEG, video, sensor data) and chemical/structural data representation (e.g. graphs, SMILES strings, molecular embeddings). Familiarity with multimodal representation learning and
-
of large-scale machine learning models (e.g., LLMs) in a meaningful way, we, therefore, need new scalable methodologies that can efficiently and accurately capture, represent, and reason about uncertainties
-
and utilize innovative, interpretable data-driven analysis methods to significantly advance our understanding of immune cell inter-relations within the cancer microenvironment. We will apply
-
microbiology assays, protein purification and biochemical assays. You will work with infection models, MS-proteomics analyses and advanced microscopy. In this role, you will belong to an interdisciplinary group
-
advanced electrode materials for aqueous battery applications, and employ various physical characterization techniques (XRD, SEM/TEM, XPS) to investigate their structure and properties. Develop and optimize
-
diabetes genetics and genomics. The methodological research may include but is not limited to statistical models using genetic data from family-based studies as well as -omics data for integrative