Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Chalmers tekniska högskola
- Lunds universitet
- Umeå University
- Karolinska Institutet (KI)
- Umeå universitet
- Umeå universitet stipendiemodul
- University of Lund
- Linköpings universitet
- SciLifeLab
- Uppsala universitet
- chalmers tekniska högskola
- Örebro University
- Chalmers Tekniska Högskola
- Karolinska Institutet
- Linköping University
- Linköping university
- Linköpings University
- Linnaeus University
- Linneuniversitetet
- Lund University
- Nature Careers
- Sveriges Lantrbruksuniversitet
- 14 more »
- « less
-
Field
-
measurement and control platform for optimal island operation of Chalmers’ wind-battery system. Machine learning-based forecasting tools for renewable production using limited local measurement data
-
University | Lund University. Ready to shape the future of research? Find more reasons why Lund University and the HT Faculties is right for you here , and learn more about Working in Lund , Moving to Lund
-
semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
-
, and doctoral students active on both campuses. Learn more about the Department of Archaeology, Ancient History, and Conservation here: Department of Archaeology, Ancient History and Conservation
-
the Division of Data Science and Artificial Intelligence and the employment is with Chalmers University of Technology. The division’s research spans from foundational machine learning theory to applications
-
Faculties is right for you here , and learn more about Working in Lund , Moving to Lund and Living in Lund . Qualifications Requirements for the position are: Ph.D. or an international degree deemed
-
mathematics, data science and machine learning for image recognition. Moreover, you will develop methods and software that will allow new characterization of nanoscale materials. Therefore, your research will
-
, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by
-
: S. Aalto). In the project we use multi-wavelength techniques, including recently developed mm and submm observational methods, to reach into the dark hearts of dusty galaxies. New machine learning
-
using genetic data from family-based studies as well as -omics data for integrative deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative