Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Chalmers University of Technology
- Linköping University
- University of Lund
- KTH Royal Institute of Technology
- Karolinska Institutet (KI)
- Lunds universitet
- Nature Careers
- Umeå University
- Umeå universitet stipendiemodul
- Chalmers tekniska högskola
- Linköpings universitet
- Linnaeus University
- Linneuniversitetet
- Lund University
- SciLifeLab
- Sveriges lantbruksuniversitet
- Umeå universitet
- chalmers tekniska högskola
- 8 more »
- « less
-
Field
-
the Job related to staff position within a Research Infrastructure? No Offer Description Job description The work involves simulations of the dynamic vehicle-track interaction for various types of rail
-
This position focuses on investigating vehicle-track-ground interaction dynamics with a particular emphasis on the critical speed induced by high-speed trains. The candidate will contribute
-
regulators of disease onset and progression. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating
-
. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating multimodal data with clinical information
-
Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
-
: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
-
approach to systematically infer protein-semiconductor hierarchies materials and design de novo hierarchical architectures with atomic precision. The postdoctoral project aims to design de novo proteins
-
presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
-
of the Saragovi Lab is to develop and apply a combined computational, Artificial Intelligence (AI) and high throughput experimental approach to systematically infer protein-semiconductor hierarchies materials and
-
, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by