Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- King's College London
- University of Minnesota
- ;
- Cornell University
- University of Washington
- CNRS
- CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences
- Illinois Institute of Technology
- Leiden University
- RCSI - Royal College of Surgeons in Ireland
- Rutgers University
- Saint Louis University
- The University of Arizona
- The University of Iowa
- University of California, Los Angeles
- Washington University in St. Louis
- 7 more »
- « less
-
Field
-
Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
-
have the experience below, please do highlight where transferable skills would assist with you undertaking the role. Qualifications PhD, or equivalent professional experience, in Machine Learning
-
parcellation (Glasser et al., 2016 Nature). The post-doc will be co-mentored by Matthew F. Glasser MD/PhD and David C. Van Essen PhD and be based in the Glasser/Van Essen laboratory in the WashU Radiology
-
at St. Stephen's Green/RCSI car park Recognition: At RCSI, we value and recognise the contributions of our staff through various awards and events, such as Long Service recognition, the Vice Chancellor
-
the ability to quickly learn new things and work independently, along with previous research experience in at least one of the following areas: 1) statistical genetics/genomics/omics, or 2) deep/machine
-
) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
-
analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job Description Primary Duties
-
candidate should have a strong background in algorithm development, transcriptomics, sequencing data processing, and/or applied machine learning. The individual will develop novel algorithms to analyze large