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The Bioinformatician is responsible for developing and applying computational methods to analyze and interpret biological data. The ideal candidate will have a strong background in biology, computer science, and statistics.


  • Develop and apply computational methods for analyzing biological data. This includes developing algorithms, software, and databases for analyzing large datasets of biological data. Bioinformaticians may work on a variety of projects, such as developing new drugs, understanding the causes of diseases, or studying the evolution of life.
  • Design and manage databases of biological information. This includes creating and maintaining databases of biological sequences, structures, and other data. Bioinformaticians may work with biologists to collect and curate data, and to develop tools for accessing and analyzing this data.
  • Develop and use software tools for biological data analysis. This includes using programming languages such as Python, R, and Java to develop software for analyzing biological data. Bioinformaticians may also work with software engineers to develop new tools for biological data analysis.
  • Collaborate with biologists, bioengineers, and other scientists to interpret biological data. This includes working with scientists to understand the biological meaning of data, and to develop new hypotheses about biological processes. Bioinformaticians may also work with scientists to communicate the results of their research to the public.
  • Communicate the results of biological data analysis to scientists and the public. This includes writing scientific papers, giving presentations, and developing educational materials. Bioinformaticians must be able to communicate complex scientific concepts in a clear and concise way.


  • Bachelor’s degree in bioinformatics, computer science, biology, or a related field
  • Experience in bioinformatics
  • Strong programming skills
  • Experience with statistical analysis software
  • Excellent communication and problem-solving skills


  • Programming languages (e.g., Python, R)
  • Database management systems (e.g., MySQL, PostgreSQL)
  • Biological data analysis software (e.g., BLAST, GWAS)
  • Statistical software (e.g., R, SAS)