GRAD SCHOLAR – Microbial Genomics/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
• 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,
• Communicate effectively by listening, documentation, and presentation.
• 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
• M.S. in Ecology and Evolution, Bioinformatics, Microbial Ecology,
Statistics, Microbial Genetics/Physiology/Ecology, plus 1+ years of
• 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
Bayer: Science For A Better Life
100 Bayer Road
Pittsburgh, PA 15205-9741, USA