Sort by
Refine Your Search
-
Category
-
Program
-
Employer
- University of Lund
- SciLifeLab
- Nature Careers
- Chalmers University of Technology
- Linköping University
- Umeå University
- Blekinge Institute of Technology
- Linnaeus University
- Mälardalen University
- ;
- KTH
- Lulea University of Technology
- Swedish University of Agricultural Sciences
- Örebro University
- 4 more »
- « less
-
Field
-
university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
-
analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
-
build the sustainable companies and societies of the future. The signal processing group carries out research in the areas of inference and wireless communications, with both acoustic and radio signals
-
systems, or network analysis. Experience with methods for causal inference, or modelling of biological systems is also considered a merit, along with prior work involving large-scale sequencing data such as
-
(causal inference and pathobiology). iii) Integrating knowledge of clinical implementation channels. Other tasks may also be assigned. Eligibility Students with basic eligibility for third-cycle studies
-
) predict molecular expressions, (ii) translate computational insight into patient-behaviour, and (iii) implement both supervised and unsupervised methods to infer stroke risk from pre-operative input data
-
and advanced causal inference methods to large-scale multi-omics datasets, national health registers, and other comprehensive health-related data sources. Duties The doctoral position is intended
-
prehistoric individuals, to make inferences about demographic history, migration and admixture patterns, and signals of adaptation. The project will include computational analysis of archaic introgression and
-
quantitative skills, a reasonable track record, and an enthusiastic approach to science. The applicant is expected to: have strong background knowledge of the physiology of primary sensory afferents and
-
, or similar, and standard purification methods. The successful candidate must have excellence in previous work and a good track record. Independence and ability to solve challenging chemical problems