Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- SciLifeLab
- University of Lund
- Chalmers University of Technology
- Nature Careers
- Linköping University
- Örebro University
- Umeå University
- Blekinge Institute of Technology
- Swedish University of Agricultural Sciences
- ;
- Mälardalen University
- Linnaeus University
- Lulea University of Technology
- Lund University
- WORLD MARITIME UNIVERSITY (WMU)
- 5 more »
- « less
-
Field
-
to galaxy evolution and observations of galactic magnetic fields. Experience with Bayesian statistics is advantageous. Strong verbal and written communication skills in English are required. To qualify
-
The Department of Statistics employs about 15 researchers, teachers, doctoral students, and other staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods
-
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
-
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
-
defenses within AI solutions (e.g., backdooring, model poisoning, membership inference), cybersecurity of generative AI / LLMs; Cyber-physical systems security, e.g., in the fields of robotics, industrial
-
methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is
-
(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
-
spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene understanding: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e
-
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