Postdoc Scientist for Machine Learning applied to Antibody Discovery and Design

This position as Postdoc Scientist offers a unique possibility to take part in the development of Servier Symphogen’s antibody products. You will explore applications of deep learning on the protein sequence space that can contribute to accelerate and improve the design and selection of therapeutic antibodies.

This position is in the Antibody Technology department and more specifically in the part of the team that works with computational design of antibodies.

The Antibody Technology department is part of the Biologics Drug Design, and all development work will be done in close cooperation with the technicians and scientists in this department.

The team is responsible for the generation of antibody repertoires from in vivo and in vitro campaigns, characterisation and selection of lead antibodies. You will be in contact with computational and wet lab scientists and technicians tackling complex challenges in antibody discovery daily. You will attend regular team meetings and participate in scientific discussions on how to design and execute drug discovery in an industry setting. Your work will be destined for integration in the pipeline through actionable algorithms and tools.

You will also be strongly connected to the computational teams located at the Servier research centre near Paris, France. Availability to travel to Paris for short visits twice a year is preferred.

Areas of responsibility

Specifically, the position is envisioned to focus on three main applications over three years:

• The first project will be dedicated to fine tuning models of antibody-target binding and affinity prediction to specific targets. You will explore strategies to best integrate knowledge of the target and already identified binders to inform the screening of new sequences. You will also explore the impact of the size and quality of training data on the performance of the models.
• The second project will focus on integrating antibody sequence and structure information to bin sequences that share the same epitope. An expansion of the project will consist in attempting to map the epitopes targeted by each bin.
• The last project will be dedicated to the exploration of methods for de novo design of antibody fragments with desirable therapeutic properties.

The order and exact definition of the projects can be subject to change based on your interest and the evolution of the field. A budget is already allocated and dedicated to the production and testing of predicted compounds.

Qualifications

You have a PhD in Computer Science, Bioinformatics, Biophysics, Computational Chemistry, Data Science or related field. You are passionate about applying machine learning methods to advance biomedical research and eager to learn about drug discovery. You are curious, rigorous and collaborative. Specifically, you have:

• Strong Python programming ability with a track record of building and training machine learning models
• Experience designing evaluation pipelines and benchmarks for novel architectures
• Experience applying computational methods to biological data, ideally protein sequence or structure data
• Experience with code version control tools
• Ability to understand and communicate complex scientific information to a broad audience of varied stakeholders and colleagues
• Proficiency in English

Preferred qualifications

• Familiarity with protein structure prediction and analysis
• Experience with Google Computing Platform and Vertex AI

We offer

• Competitive salary based on qualifications, and flexible working hours
• Strong cross functional teamwork, room for individual performance and development, and passionate, inspiring, and fun colleagues

Working in Symphogen

Being part of Symphogen, you will always have a challenging job in a creative, goal-oriented, and value-based environment with a meaningful mission – to advance superior mAb therapeutics to improve the lives of patients. You will be a part of a highly engaged and inspiring organization with focus on diversity, teamwork, commitment – and on you as a person. You will be offered good development opportunities and a relevant benefit package.

Domicile: Ballerup, Copenhagen

This is a 18-month temporary position, renewable once based on supervisor recommendation for a total of 3 years.

For further information contact Jonathan Desponds, Senior Bioinformatics Scientist, Antibody Technology, jonathan.desponds@servier.com or Senior HR Manager, Pia Holm Olesen at +45 4526 5050. Please also see Frontpage – Servier Symphogen for information about our company.

If you are interested in applying for the position, please mark your application and CV “Postdoc_deep_learning” and mail it to: jobs-dk2@servier.com no later than November 15th, 2025. Potential candidates will be invited for interview as they apply, and we reserve the right to terminate before the posting expires. All applications must be in Danish or English and are treated confidentially.