Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Linköping University
- Chalmers University of Technology
- University of Lund
- KTH Royal Institute of Technology
- SciLifeLab
- Blekinge Institute of Technology
- Lunds universitet
- Nature Careers
- Umeå University
- Lulea University of Technology
- Uppsala universitet
- Linnaeus University
- Mälardalen University
- Swedish University of Agricultural Sciences
- Karlstad University
- Karolinska Institutet (KI)
- NORDITA-Nordic Institute for Theoretical Physics
- Umeå universitet
- 8 more »
- « less
-
Field
-
funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new machine learning methods for multimodal data and
-
. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
-
applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
-
modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
-
application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
-
, contribute to a better world. We look forward to receiving your application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and
-
sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock
-
application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
-
of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
-
datasets for analysis. Implementing and improving deep learning models for detecting and mapping forest disturbances. Validating model performance using reference datasets and ground truth information from