Research Scientist Data Science, Bioinformatics & Genomics (m/f/d)

 


At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.


 


Research Scientist Data Science, Bioinformatics & Genomics (m/f/d) 


 

The Applied Mathematics group within Bayer is looking for a scientist with expertise in data science and genomics to join Bayer Engineering & Technology in Leverkusen, Germany. The successful applicant will be part of an interdisciplinary and cross-divisional team within the R&D organization and will contribute to the implementation of novel algorithms and models for the predictions of phenotypes of genetically modified organisms. In detail:


 


We offer two positions in this area. We expect that the applicants have equally strong backgrounds in data science, while they supplement each other in their domain expertise regarding mammalian or plant biology and genomics.


 


YOUR TASKS AND RESPONSIBILITIES


  • To increase the efficiency of our R&D-function (Research & Development), you develop application driven models to predict the effect of transgene integration on mammalian cell lines and plants
  • You work on the interface of molecular biology, cell culture and high throughput omics as well as data science and bioinformatic and you develop models that bridge both worlds
  • You design, compose and deliver presentations and publications
  • You will be part of an international and interdisciplinary cross-divisional team comprising of cell and molecular biology, bioprocess engineering, mathematicians, and machine learning experts


 


WHO YOU ARE


  • Master or PhD in natural science or computer science with strong background in bioinformatics, machine learning and data driven modeling
  • Profound knowledge in genomics as well as in mammalian or plant
  • You have extensive experience in developing data science approaches in the context of omics technologies (e.g. genome sequencing, transcriptomics, epigenetics)
  • You are familiar with state-of-the-art bioinformatics, machine learning methods and model selection and have a strong willingness to further develop expertise in these areas
  • Profound knowledge in Python, R, Linux
  • Willingness and curiosity to learn experimental set-ups, novel mathematical approaches, as well as to dig into Pharma- and Crop Science-R&D
  • You are able to work in interdisciplinary teams with excellent interpersonal and communication skills
  • High level of English communication skills in verbal and written form


 


Funding of this position is made available through the Bayer Life Science Collaboration program. The goal of this program is to promote state-of-the-art research within Bayer´s R&D organization especially focusing on cross-divisional exchange and impact.


 


The position is limited for 2 years.


 


 

YOUR APPLICATION


This is your opportunity to tackle the world’s biggest challenges with us: Maintaining our health, feeding growing populations and slowing the rate of climate change. You have a voice, ideas and perspectives and we want to hear them. Because our success begins with you. Be part of something big. Be Bayer.

Bayer welcomes applications from all individuals, regardless of race, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, pregnancy, sexual orientation, gender identity, gender expression or any unlawful criterion under applicable law. We are committed to treating all applicants fairly and avoiding discrimination.

 


Location:            ​ Leverkusen


Division: ​               Enabling Functions


Reference Code:  478444


 

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