Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- Linköping University
- Lunds universitet
- SciLifeLab
- KTH Royal Institute of Technology
- Umeå University
- Uppsala universitet
- Jönköping University
- KTH
- Lulea University of Technology
- Umeå universitet stipendiemodul
- Göteborgs Universitet
- IFM/Linköping University
- Karolinska Institutet, doctoral positions
- Linköpings universitet
- Nature Careers
- Sveriges Lantbruksuniversitet
- Umea University
- Umeå universitet
- University of Borås
- 10 more »
- « less
-
Field
-
degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/ ). The research team focuses on developing novel methods
-
contextualized by existing expertise in existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will
-
situated in the field of machine learning. Potential research topics include, but are not limited to, algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine
-
algorithms are agnostic of the downstream task they will be deployed on, and this may lead to a suboptimal control performance. In this project, we will investigate control-oriented biases and their impact on
-
international conferences or journals, especially publications in the area of Air Traffic Management Knowledge of optimization techniques Knowledge in the area of design and analysis of algorithms Great emphasis
-
to the fundamentals and algorithms of spatially and time-multiplexed oscillator-network computing. Duties The PhD student will focus on the fundamentals and algorithms for spatially and time-multiplexed oscillator
-
perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
-
equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
-
for fast tune-up Implementation and benchmarking of quantum algorithms Qualifications We are seeking candidates with: A PhD in Physics, Applied Physics, Nanotechnology, Computer Science, Engineering, or a
-
, epidemiological data and outcome modelling using AI-assisted algorithms, as well as multi-modal data integrations. An established data infrastructure with expertise and computational pipelines for these analyses