26 coding-"https:"-"FEMTO-ST"-"CSIC"-"P"-"U"-"U.S"-"https:" positions at Nature Careers in United States
Sort by
Refine Your Search
-
, atmospheric/environmental science, meteorology, geoecology, or related field Excellent coding and quantitative analytical skills, preferably in R, Matlab, or Python In-depth knowledge of micrometeorological
-
epigenetic basis of GI cancers (e.g., aberrant DNA methylation, histone modifications, and non-coding RNAs). Understanding the biological implications of gut microbiome and its translational application is
-
imaging). Basic coding familiarity (Python, Julia, R, or Matlab). Excellent oral and written communication skills, including the ability to analyze data and prepare figures suitable for publication. Strong
-
epidemiology, pharmacogenomics, statistical genetics, or population genetics and experience in statistical and computational analyses of high-throughput omics data Ability to code in one or more scientific
-
targeted drugs. Studying the genetic and epigenetic basis of GI cancers (e.g., aberrant DNA methylation, histone modifications, and non-coding RNAs). Understanding the biological implications of gut
-
The Department of Biotechnology and Food Science, Institute of Computational Biology is currently seeking a Postdoctoral Research Associate (Reference code 216) Extent of employment: 40 hours per
-
/ lab notebook management. Preferred Qualifications: Familiarity with a coding language (Python or others). Compensation Range: Research Technician II (annual) – $47,840 (minimum) - $59,800 (midpoint
-
benchmarked through comparisons with traditional PSHA models. The work will involve developing reproducible open-source codes and participating in an international research network linking seismology, geodesy
-
architecture that you trained yourself. If it is in the sequence or protein structure domain, even better! If possible, include a link to a code repository. If you are a contributor to a joint project, that is
-
develop dataset processing algorithms, for in-acquisition and proprietary datasets, including in federated training settings. Day-to-day: Develop new ideas, write code, run experiments, analyze data, and