Sort by
Refine Your Search
-
Listed
-
Employer
- SciLifeLab
- KTH Royal Institute of Technology
- Lunds universitet
- Chalmers University of Technology
- Karlstad University
- Blekinge Institute of Technology
- Umeå University
- Karolinska Institutet (KI)
- University of Lund
- Chalmers tekniska högskola
- Karlstads universitet
- Linköping University
- Örebro University
- Linnaeus University
- Stockholms universitet
- University of Gothenburg
- Uppsala universitet
- Chalmers Tekniska Högskola AB
- Department of Chemistry and Molecular Biology, University of Gothenburg
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg
- Linneuniversitetet
- Luleå University of Technology
- Malmö universitet
- Mälardalen University
- Stockholm University
- The University of Gothenburg
- Umeå universitet
- 17 more »
- « less
-
Field
-
able to understand better how and why toxic oligomers form. We are also interested in using our technologies to study enzymes, which are nature's catalytic machines. Enzymes are very important for
-
mobile manipulation tasks. We are seeking candidates with a strong background in robotics and machine learning, and demonstrated experience in two or more of the following areas: deep learning
-
spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
-
collected can be trusted for training machine learning (ML) models and run-time interference. Both the use of AI in products, as well as the collection of data, assume fast iterations that allow for rapid and
-
phenotypes, based on both statistics/machine learning and computational mechanistic modelling. The group specializes in analysing complex OMICs datasets (e.g., transcriptomics, proteomics, microbiota
-
of the identified structures via stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and
-
development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The position is aimed at researchers early in their career
-
: Experience combining proteomics with genomic/transcriptomic data Specialized knowledge: Understanding of peptide-spectrum matching, FDR estimation, protein inference AI/ML proficiency: Experience with machine
-
integrated part of both centres, with focus on new methods for analysing and modelling molecular data, cellular mechanisms and clinical phenotypes, based on both statistics/machine learning and computational
-
to measure these backgrounds in data. The project also aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal vs background discrimination