European Molecular Biology Laboratory (EMBL) invites applications for a Postdoctoral position in machine learning and computational single-cell genomics in the Statistical Genomics and Systems Genetics group at the Genome Biology Unit at EMBL. The position is funded via the recently awarded BMBF project MechML and will be jointly located at EMBL Heidelberg and DKFZ.

Role and responsibilities:
  • develop novel machine learning models that integrate mechanistic prior information and knowledge in a principled manner
  • apply these methods to large single-cell variation datasets with millions of cells, and to integrate spatial technologies with single-cell RNA-seq and epigenome methods
  • contribute to the Human Cell Atlas, as a node in the analysis working group
  • collaborate with partners in the MechML projects, the Human Cell Atlas, collaborators at EMBL, DKFZ and elsewhere
  • build on previous developments and expertise in the group, including factor model, linear mixed models and deep learning methods

Duration: 3 years

Requirements:

  • applicant must hold doctoral degree or equivalent qualification in Computer Science, Statistics, Mathematics, Physics, and/or Engineering, or a degree in biological science
  • Applicant must have some previous experience in developing and applying computational methods applied to large datasets and experience in developing statistical methodology in a genomics context, including gene expression analysis, factor models, GWAS and analysis of NGS data.
  • Applicant with previous usage of methods in any of the following fields is advantageous: statistics, machine learning, genetics, optimization and mathematical modeling
  • Applicant must possess proficiency with a high-level programming language (e.g., C++, Java) and/or appropriate scripting languages, and statistical data analysis tools such as R, MATLAB or Python

How to apply:

Apply online

—————–Quick Overview————-
OrganizationEuropean Molecular Biology Laboratory (EMBL)
CountryGermany
Fellowship LevelPostdoctoral
Subject areas

Computer Science, Statistics, Mathematics, Physics, Engineering, Biological Sciences

Fellowship amountVaries
EligibilityOpen to all nationalities
Deadline20 November 2018
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