Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
statistics of mean and extreme wind events over the Baltic Sea region. Tellus A, 67, 29073. https://doi.org/10.3402/tellusa.v67.29073 Björkqvist, J.-V., Lukas, I., Alari, V., van Vledder, P.G., Hulst, S
-
larger spatial scales using different data sets. The postdoctoral fellow will be responsible for analyzing the data sets; developing, interpreting, and applying the two statistical models; and submitting
-
data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and computational in nature and will include: Developing minimal active-matter
-
spatial resolution, phase sensitivity, temporal stability, and reconstruction accuracy. Integrate experimental measurements with computational reconstruction pipelines to enable robust and accurate 3D
-
using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
-
, College of Department: Data Science Rank: Associate Professor Annual Basis: 9 Month Application Deadline October 31, 2025 Required Application Materials Candidates are asked to apply online at https
-
omics. Nat Methods 22, 58–62 (2025). https://doi.org/10.1038/s41592-024-02212-x Crowell, H. L., Dong Y., Billato I. et al. Orchestrating Spatial Transcriptomics Analysis with Bioconductor. bioRxiv
-
of novel probabilistic deep-learning models that automatically extract mechanistic and statistical knowledge from your in vivo perturbational omics data. This interdisciplinary atmosphere has been a main
-
in spatial analyses Excellent scientific writing skills, including ability to lead high quality multiple-authored journal papers in an efficient manner Strong ability to work independently and to
-
: Analysis and interpretation of high-resolution single-cell RNA-seq and scATAC-seq data to elucidate cellular processes Processing and integration of spatial transcriptomics data to reveal molecular patterns