Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- SciLifeLab
- Umeå University
- KTH Royal Institute of Technology
- Karolinska Institutet (KI)
- Linköping University
- Lunds universitet
- Umeå universitet stipendiemodul
- Blekinge Institute of Technology
- Luleå University of Technology
- Uppsala University
- Uppsala universitet
- 2 more »
- « less
-
Field
-
Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
-
advanced analytical approaches, including statistical modelling, machine learning, and/or genetic epidemiological methods, to study disease trajectories, risk factors, treatment patterns, and sources
-
of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
-
of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
-
patients with pancreatic cancer and other aggressive tumor types. The work includes statistical modelling, machine-learning and systems biology approaches to identify drivers of metastasis and treatment
-
machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
-
dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
-
. Beyond Discrete Mathematics, the Department of Mathematics and Mathematical Statistics carries out research in computational mathematics, financial mathematics, mathematical modeling, analysis, machine
-
regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
-
. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop independence as a researcher and to create the opportunity