GRAD SCHOLAR – MicrobialGenomics/Machine Learning

Job Description:

GRAD SCHOLAR - MicrobialGenomics/Machine Learning
To support R&D projects within Biologics Bayer CropScience (BCS), to
drive innovative crop protection and plant health solutions, and to
develop and implement b data analysis tools and algorithms.  The
candidate will work closely with wet lab scientists and computational
scientists in West Sacramento, CA and other scientists throughout BCS.

Position: Major Tasks
- Proactively identifying and incorporating novel statistical
  methodologies to link bacterial taxonomy/genomics to function.
- Participate in a multi-disciplinary team of scientists who offer
  comparative genomics, pathway modeling, network analyses, and
  metagenomics for controlling pests and diseases in plant and promoting
  plant health using microbes.
- Conduct research and collaborate with scientists using machine
  learning methodologies to examine microbial processes and mechanisms
  that underlie plant-microbe interactions, produce secondary
  metabolites, and contribute to primary microbial metabolism.
- Help drive the experimental design, analysis, and interpretation of
  HTS datasets incorporating total community analysis(functional gene
  analysis, phylogenetic and network analysis), comparative genomics, de
  novo assembly of targeted specific community,genes and selected
  microbial genomes.
- You will be joining a computational life sciences team which
  bringstogether expertise in biology, computational science,
  statistics,bioinformatics and software development.
- Be able to communicate effectively through listening,
  documentation,and presentation, especially using compelling
  visualization tools to share analysis and interpretation of data.
- Provide analysis and feedback about experimental results to
  supervisors, highlighting important results and defining next step
  experiments.
- Coordinate and cooperate on research activities with peers,
  supervisors, and subordinates
- Communicate effectively by listening, documentation, and presentation.

Position: Skills
- PhD in Ecology and Evolution, Microbial Ecology, Microbial
  Genetics/Physiology/Ecology, Statistics, Applied Statistics, Machine
  Learning (or nearing substantial completion, provided all Ph.D.
  requirements are successfully completed within 6 months of employment
  start date).
- M.S. in Ecology and Evolution, Bioinformatics, Microbial Ecology,
  Statistics, Microbial Genetics/Physiology/Ecology, plus 1+ years
  of relevant experience.
- Proven ability to work within a reproducible framework, handling large
  data sets efficiently using scripts,databases, and other tools;
- Should be highly versed in experimental design methodologies, mixed
  linear modeling, and machine learning and be able to communicate the
  output with other scientists around interpretation of these
  statistical analyses.
- Knowledge of R or Python is essential.
- Knowledge of other programming languages is a plus (Unix, Perl, C,
  C++)
- Knowledge of microbial physiology an asset.


Janette Gardiner <janette.gardiner@bayer.com>

Requeriments :

• M.Sc. or higher Degree in Bioinformatics
• M.Sc. or higher Degree in Computer Science

Skills :

• Bioinformatics
• Biology
• Machine Learning

Areas :

• Bioinformatics

[Click Here to Access the Original Job Post]