Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Chalmers tekniska högskola
- Lunds universitet
- Umeå University
- Umeå universitet stipendiemodul
- University of Lund
- Uppsala universitet
- Karolinska Institutet (KI)
- SciLifeLab
- Örebro University
- Umeå universitet
- KTH
- Linköpings universitet
- chalmers tekniska högskola
- Chalmers Tekniska Högskola
- European Magnetism Association EMA
- Karolinska Institutet
- Linköping university
- Linköpings University
- Linneuniversitetet
- Nature Careers
- Sveriges Lantrbruksuniversitet
- 13 more »
- « less
-
Field
-
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
-
, 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
-
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
-
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
-
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
-
, 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
-
academic research, learning and outreach. We provide a competitive advantage by linking our top-level international and interdisciplinary academic performance in the areas of material science, nanotechnology
-
deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative research may include but is not limited to software tool dissemination, biology discovery, and
-
experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
-
information and communication theory, machine learning, and signal processing. We offer a dynamic, supportive, and international research environment with around 150 employees from more than 20 countries. Our