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Intern in computational biology (all genders)

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Bayer

12d ago

  • Internship
    Full-time
    Off-cycle Internship
  • Data
    Research & Development
  • Berlin

AI generated summary

  • You should have a relevant degree, data analysis experience, programming skills in R/Python, interest in biomedical research, strong English skills, and ideally experience with genetic data analysis.
  • You will analyze genetic and proteomic datasets, prepare data, develop analysis strategies for gene/protein pathways, and collaborate with researchers to interpret findings.

Requirements

  • Bachelor’s degree in relevant quantitative discipline (e.g.: bioinformatics, genetics, mathematics, statistics, data science, physics, computer science, quantitative social sciences)
  • Some experience with data pre-processing, data analysis/modelling
  • Good programming skills (R and/or Python preferred)
  • Interest in biomedical and clinical research questions
  • Strong written and verbal English skills
  • Preferred: experience with analyzing genetic data

Responsibilities

  • Identification of and insight generation from relevant publicly available or internal datasets with both genetic and proteomic data or genetic and gene expression data.
  • Data preparation and data homogenization.
  • Development and implementation of a data-driven analysis strategy to identify downstream and upstream proteins/genes in a pathway.
  • Close exchange with researchers for the interpretation of the findings.

FAQs

What is the duration of the internship?

The internship is for a period of 6 months.

Where is the internship located?

The internship is located in either Berlin or Wuppertal, Germany.

What tasks will I be responsible for during the internship?

You will be responsible for identifying and generating insights from relevant datasets, data preparation and homogenization, developing a data-driven analysis strategy, and exchanging closely with researchers to interpret findings.

What academic background is required for this internship?

A Bachelor’s degree in a relevant quantitative discipline such as bioinformatics, genetics, mathematics, statistics, data science, physics, computer science, or quantitative social sciences is required.

What programming skills are preferred for this position?

Good programming skills in R and/or Python are preferred.

Is prior experience in analyzing genetic data required?

Prior experience in analyzing genetic data is preferred but not explicitly required.

What kind of work environment can I expect?

You can expect a friendly and team-oriented working atmosphere.

How should I apply for the internship?

You should apply online with your complete documents, including a cover letter and CV.

When does the internship start?

The start date will be agreed upon during the interview.

Does Bayer welcome applications from individuals of all backgrounds?

Yes, Bayer welcomes applications from all individuals regardless of race, national origin, gender, age, physical characteristics, and other criteria, and is committed to treating all applicants fairly.

Bayer

Science for a better life

Science & Healthcare
Industry
10,001+
Employees
1863
Founded Year

Mission & Purpose

Bayer is a global enterprise with core competencies in the Life Science fields of health care and agriculture. Our products and services are designed to benefit people and improve their quality of life. At the same time, we aim to create value through innovation, growth and high earning power. Our products help address some of today’s biggest challenges, including global population growth, an aging society and the need to make efficient – and, wherever possible, sustainable – use of natural resources. In line with our mission “Bayer: Science For A Better Life,” we aim to improve people’s quality of life by preventing, alleviating or curing diseases. We also help provide an adequate supply of high-quality food, feed and renewable plant-based raw materials. For these endeavors, we focus on developing and successfully commercializing innovative products and solutions based on scientific knowledge.