225 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" Postdoctoral positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
pediatric cancers. In prior work, we have developed tools and methods to map the cellular diversity of pediatric tumors by adapting single-cell sequencing techniques to archival frozen tumors. In that work
-
spanning multiple diseases. About the lab: The Glastonbury Lab is focused on developing and applying Machine Learning to problems in digital pathology and spatial transcriptomics. The group has a particular
-
contributing to specifically the area of handling spatial data to assess the distribution of several soil properties and fungal communities using samples collected from multiple habitats and land use types at a
-
· Gender-friendly environment with multiple actions to attract, develop and retain women in science · 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
-
and in vivo model systems, applying multiple omics methods. You will be working with clinical samples, method development and several molecular biology techniques, especially PCR and sequencing as
-
postdoctoral research position is opening to study the mechanisms underlying the response of luminal breast cancers to endocrine and cell cycle targeted therapies ● The project will involve development and
-
activity in ulcerative colitis patients with transcriptional changes in a longitudinal patient cohort, develop deconvolution algorithms, extract features from H&E sections etc. Bacterial metabolism and host
-
mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
-
studies that extend across multiple scales. The Pioneer Center Land-CRAFT was established in June 2022 to undertake fundamental and applied research from field to landscape scales that will address
-
to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage