Sort by
Refine Your Search
-
Category
-
Program
-
Employer
- SciLifeLab
- Chalmers University of Technology
- Umeå University
- KTH Royal Institute of Technology
- University of Lund
- Linköping University
- Lulea University of Technology
- Luleå University of Technology
- Lunds universitet
- Uppsala universitet
- Helmholtz-Zentrum München
- Karolinska Institutet, doctoral positions
- Linnaeus University
- Swedish University of Agricultural Sciences
- The Swedish University of Agricultural Sciences
- The University of Skövde
- Umeå universitet
- Uppsala University
- universitypositions
- 9 more »
- « less
-
Field
-
of the central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding. The goal is to develop new
-
systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
-
, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
-
high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
-
analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark
-
of the world’s largest research environments in computational science, with large activities in areas such as machine learning, optimization, scientific software development and high-performance computing
-
, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
-
and pair distribution function (PDF) analysis on carbon materials electron microscopy (SEM and/or TEM) data analysis, machine learning and molecular dynamics simulations of the structure and
-
machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates will have the opportunity to collaborate closely
-
for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates