Computational Biologist

Company Description

AIxBIO is dedicated to revolutionizing biomanufacturing, making it more intelligent, adaptive, and efficient. We address the inefficiencies of traditional fermentation processes by leveraging innovative approaches. By integrating mathematical models, computational fluid dynamics, and artificial intelligence, we create cutting-edge solutions. Our mission is to empower our partners and drive advancements in fermentation technology towards unparalleled success. We have recently secured pre-A round funding and are now looking to grow our team.

Role Description

This is a full-time, on-site role located in Gothenburg (Sweden) for a Computational Biologist. The role involves designing, developing, and implementing computational models to analyze biological systems. In particular, you will work with constraint-based models of metabolism, gene-expression and bioreactor cultivation data, among others. You will collaborate within a cross-functional team to conduct data-driven research, analyze experimental data, develop innovative bioinformatics tools, and design workflows to address complex biological questions linked to fermentation technology.

Mandatory Qualifications

• Experience in (Computational) Metabolic Engineering and/or Metabolic Systems Biology.
• Proficiency in Data Science and Bioinformatics, including experience with computational tools and algorithms.
• Strong research skills to develop and validate computational models and workflows for biological studies.
• Experience with programming languages commonly used in computational biology, such as Python, R and/or MATLAB, is preferred.
• Ph.D. or equivalent qualification in Biotechnology, Bioinformatics, Computational Biology, Systems Biology, or a related field.
• Excellent problem-solving skills, teamwork capabilities, and a passion for innovation in the biotechnology domain.

Desirable Qualifications

• Hands-on experience with bioreactor cultivations, metabolic engineering, genetic engineering, microbiology.
• Hands-on experience with genome-scale constraint-based models of metabolism.
• Hands-on experience with machine learning with biological data.
• Hands-on experience with computational fluid dynamics or transport phenomenon modelling.