Sort by
Refine Your Search
-
Category
-
Employer
- Linköping University
- Karolinska Institutet, doctoral positions
- Umeå University
- Lulea University of Technology
- Linköpings universitet
- Lule university of technology
- Luleå University of Technology
- Luleå university of technology
- Mid Sweden University
- Nature Careers
- Swedish University of Agricultural Sciences
- 1 more »
- « less
-
Field
-
principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
-
application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
-
well as physically-based hydrological model development. The principal supervisor will be Ylva Sjöberg at Umeå University, and the research involves an interdisciplinary team of collaborators at Gothenburg University
-
. 10 PhD vacancies are available in six universities. For more information, regularly check the individual vacancy pages of the universities listed on the project website [https://heritour.eu/ ]. About
-
scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
-
model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed direction of the PhD studies will be discussed and decided jointly with
-
and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
-
environmental triggers, such as common viral infections and microbial exposures. With a strong translational focus, the group integrates cutting-edge molecular, cellular, and model systems to bridge fundamental
-
transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
-
cytometry, genome editing, and functional tumor models to decipher and engineer cytotoxic lymphocyte responses in cancer. We collaborate closely with clinical units at Karolinska University Hospital and