Sort by
Refine Your Search
-
Listed
-
Employer
- KTH Royal Institute of Technology
- Chalmers University of Technology
- Chalmers tekniska högskola
- Karolinska Institutet (KI)
- Lunds universitet
- Umeå universitet stipendiemodul
- Umeå University
- University of Lund
- Linköpings universitet
- SciLifeLab
- Umeå universitet
- Uppsala universitet
- chalmers tekniska högskola
- Örebro University
- Karolinska Institutet
- Linköping university
- Linnaeus University
- Linneuniversitetet
- Nature Careers
- Sveriges Lantrbruksuniversitet
- 10 more »
- « less
-
Field
-
, 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
-
and feedback; 4- numerical techniques for modeling galaxy evolution, machine learning and AI techniques . The research team is part of the growing and vibrant research environment encompassing
-
presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
-
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
-
description and duties The postdoc fellow will conduct research at the borderline between the fields of information visualization / visual analytics as well as machine learning in close collaboration with
-
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
-
combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model
-
ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
-
technology. We are located on LTH's campus in northern Lund. At the Division of Electromagnetics and Nanoelectronics within the Department, we develop and study new generations of electronics based on advanced
-
data reflect real‑world disease phenotypes. Advanced analytics: apply AI and machine‑learning techniques (e.g., graph neural networks, multimodal transformers) to uncover novel biomarkers and generate