Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- ;
- Duke University
- Temple University
- University of Pennsylvania
- CNRS
- FCiências.ID
- Faculty of Sciences of the University of Porto
- Heraeus Covantics
- McGill University
- Nature Careers
- THE UNIVERSITY OF HONG KONG
- Technical University of Munich
- The Ohio State University
- Universidad de Alicante
- University of Alabama at Birmingham
- University of Pittsburgh
- University of Utah
- VU Amsterdam
- Virginia Tech
- 9 more »
- « less
-
Field
-
-Spain’s Recovery, Transformation and Resilience Plan 1. First project in which the successful applicant will collaborate: 1.1. Name of the project: “Impact of the spatial-temporal aggregation
-
machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
-
Biosafety Lab Alabama Birmingham (SEBLAB) having BSL-3 laboratories, ABSL-3 animal facility, high-parameter flow cytometry and cell sorting cores, and UAB's nationally recognized spatial omics and proteomics
-
- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin
-
imaging with image analysis, and spatial genomics and proteomics. The position will be responsible for participation in seminars, professional development opportunities, manuscript preparation and
-
. The place of work: The tasks will be developed in the Department of Geosciences, Environment and Spatial Plannings of the Faculty of Sciences of the University of Porto. 6. Duration of work permanent contract
-
-scale job ads datasets, spatial datasets, patents). Conduct data analysis using econometric and statistical tools. Excellent knowledge of R is expected. Good knowledge of Python, experience with modern
-
spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
-
focus. Example learning problems include exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have
-
characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and