Senior Scientist – Integrated in Silico Antibody Engineering (m/f/d)

Senior Scientist – Integrated in Silico Antibody Engineering (m/f/d)

Servier Symphogen is inspired by nature, led by science, and driven by people.

Servier Symphogen is the Antibody Center of Excellence within the Servier Group. We combine computational methods, antibody engineering, and experimental workflows to support the discovery and development of differentiated therapeutic antibodies.

We are looking for a Senior Scientist to join the Computational Antibody Design team within the Antibody Technology department.

This position focuses on the development and integration of machine learning methods supporting antibody engineering, candidate optimization, and experimental decision-making workflows. You will work at the interface between computational and experimental research, contributing to DMTA/DMTL cycles, active learning strategies, and data-driven antibody optimization approaches.

Key responsibilities

• • Conduct innovative research in antibody discovery with a focus on integrated machine learning approaches for antibody engineering
  • Develop and implement computational models for antibody property and function prediction supporting antibody optimization and candidate selection
  • Integrate computational methods into experimental workflows and therapeutic project decision-making processes
  • Contribute to the development of computational approaches supporting DMTA/DMTL cycles for antibody property, format, and function optimization
  • Develop methods for optimal experimental design, including Bayesian approaches and information-driven strategies
  • Support the implementation of active learning approaches within antibody discovery workflows
  • Collaborate with cross-functional teams to integrate computational findings into therapeutic programs and platform development activities
  • Work closely with experimental scientists, technicians, bioinformaticians, data scientists, and ML engineers across research programs
  • Establish and contribute to collaborations with academic and industry partners

Required Qualifications

• • Ph.D. in Bioinformatics, Computational Biology, Data Science, Computational Chemistry, Biophysics, Computer Science, or a related field
  • Experience in computational drug discovery or related interdisciplinary research environments
  • Experience with protein property prediction and optimization models
  • Strong understanding of machine learning and statistical methods, including Bayesian approaches
  • Strong interest in experimental antibody discovery workflows and experience collaborating with wet-lab scientists
  • Experience with active learning, information-driven optimization approaches, or DMTA workflows is considered advantageous
  • Strong Python programming skills and familiarity with version control systems
  • Ability to communicate scientific concepts effectively in a multidisciplinary research environment
  • Collaborative, rigorous, and scientifically curious working style

 What Servier Symphogen offers

• • An integrated computational and experimental antibody research environment
  • Close collaboration across scientific disciplines and international research teams within Servier  Symphogen and Servier Group
  • Opportunities to contribute directly to therapeutic antibody discovery and platform development
  • This position is offered as a 3-year temporary contract
  • Professional development opportunities within an international research organization
  • Flexible working conditions and competitive compensation

Application
The recruitment is outsourced to external Recruiter Aims International Denmark.

To apply for this job, email your details to Ferhan Cetinkaya – f.cetinkaya@aims-germany.com | aimsinternational.com quoting project number “DK-0025p”

AIMS International-Denmark ApS
World Trade Center Ballerup

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