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Senior Research Scientist Bioinformatics
exscientia
5 months ago
Negotiable
Full-time
Vienna, Austria
Description

The translational bioinformatics team in Vienna is looking to recruit an experienced bioinformatician as part of our team dedicated to uncovering novel patient stratification and selection markers, with a focus on cancer and systems biology. You will take the lead in biomarker discovery and multi-modal data integration projects, combining large scale sequencing and other data with our unique ex-vivo translational screening platform.

Exscientia is committed to getting better medicines to the right patients. We do this by applying the latest research in Artificial intelligence (AI), machine learning, and high-performance computational infrastructure to transform drug design. Exscientia developed the first-ever functional precision oncology platform to successfully guide treatment selection and improve patient outcomes in a prospective interventional clinical study, as well as to progress AI-designed small molecules into the clinical setting.

As part of our Translational Bioinformatics team, you will be at the forefront of translational cancer research that will have a real impact on patients’ lives.


You Will Have The Opportunity To:

  • Plan and carry out the analysis of in-house generated multi-dimensional sequencing data (e.g. WES / WGS, single-cell RNA-seq) to answer critical questions in drug MoA and biomarker discovery.
  • Integrate genomic, transcriptomic and epigenetic data with cutting edge high-throughput screens from primary patient samples to decipher factors of drug sensitivity in patients.
  • Use a cutting-edge distributed cloud environment with a high degree of autonomy.

Requirements

  • PhD in bioinformatics or a related field with at least 4 years of experience (postdoc or industry)
  • Strong background in data analysis and interpretation of multi-modal data in oncology, with an emphasis on next-generation sequencing (NGS) data
  • Experience using key cancer biology databases and resources (e.g. TCGA)
  • Highly proficient with R or Python
  • Strong English communication skills
  • Experience working remotely in Linux / UNIX environments


Ways to stand out:

  • Experience in applying advanced ML methods to sequencing data, or feature engineering to drive advanced ML applications in a multi-disciplinary team
  • Advanced understanding of cancer biology and immunology
  • Experience using one of the major cloud platforms (AWS, GCP, Azure)
  • Familiarity with workflow managers and languages (e.g. NextFlow, WDL, SnakeMake)

Benefits

  • Work together to make a positive contribution to patients by revolutionising the pharmaceutical industry through AI-driven discovery.
  • We will work with you to kick-start or further grow your career – from funding for professional development and conference attendance. To also ensuring you will have the opportunity to acquire valuable skills, work on problems that matter, and learn from world-class technical and scientific leaders.
  • Opportunity to join an inclusive, collaborative and intellectually stimulating culture
  • A highly competitive compensation package in accordance with the Austrian collective bargaining agreement, starting from a minimum of 45.000,-€ gross per year. We will negotiate an attractive package and overpayment based on your background and previous work experience.
  • Our team’s health and well-being is important to us we offer a generous holiday allowance, provide flexible working and remote working to encourage all of our team to manage their own work, time and life along with wellbeing and mindfulness support.
  • We offer daily lunch, regular team events and lots of opportunities for casual networking.
  • Hear from our team why they enjoy working here
See more...
About the company
exscientia
The translational bioinformatics team in Vienna is looking to recruit an experienced bioinformatician as part of our team dedicated to uncovering novel patient stratification and selection markers, with a focus on cancer and systems biology.
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