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In Roche’s Pharmaceutical Research and Early Development organisation (pRED), we make transformative medicines for patients in order to tackle some of the world’s toughest unmet healthcare needs. At pRED, we are united by our mission to transform science into medicines. Together, we create a culture defined by curiosity, responsibility and humility, where our talented people are empowered and inspired to bring forward extraordinary life-changing innovation at speed.
As a Postdoctoral Fellow (m/f/d) in the Roche Pharma Research and Early Development, jointly hosted by the Lead Identification Department and Computational Protein Engineering Department, you will focus on developing and validating novel computational methods for antibody engineering and optimization, with the overarching goal to develop tomorrow's medicine.
We are offering an outstanding opportunity for a Postdoctoral Fellow to combine state-of-the-art in silico methods with experimental wet-lab work and be part of a highly engaged team of antibody engineering and computational modelling specialists.This project is centered on integrating Machine Learning (ML) with established Molecular Dynamics (MD) workflows. A core component of this position is the lab-in-the-loop approach, which includes both computational modeling and experimental validation (e.g., SPR, DSF) of the proposed mutants in the wet lab.
As a postdoctoral fellow (m/f/d), you will be supervised by scientists at Roche and an independent academic expert as a partner. The duration of the RPF project is initially set for two years, with the possibility of an extension for a third year. The position is based in Penzberg, Germany.
Your Impact:
You will actively participate in developing a predictor for antibody affinity maturation and developability.
You will contribute to cutting-edge drug discovery efforts to develop breakthrough medicines.
You will attend (and present at) scientific meetings, interact with the scientific community and publish experimental advances after appropriate IP-protection.
Collaborate with the host teams and other stakeholders to develop and evaluate state-of-the-art protein design algorithms
Who you are:
First of all, you have a solid understanding of protein biophysics
You have hands-on experience with core laboratory techniques for creating protein variants, such as protein expression and purification or the eagerness to learn
A PhD in a relevant field, such as Computational Structural Biology, Bioinformatics, Biophysics, Biochemistry or a related field with a strong computational focus is the base of your expertise
We can trust in your deep understanding of machine learning approaches for protein structure and property prediction, as well as of generative models
Structural modeling and design platforms for biomolecules and protein-protein interactions (e.g. MOE, Schrödinger, Rosetta, modeling and docking algorithms) are even so well known to you
You are fluent in English, have strong oral and written communication skills and a record of scientific excellence, as evidenced by respective publications.
Additional Experience and Competencies Preferred:
Solid understanding of antibody structure and/or familiarity with immune repertoire sequencing datasets
Hands-on experience of in vitro testing methods for antibody characterization, such as Surface Plasmon Resonance (SPR)
You have been actively engaged in scientific research continuously since your PhD and are ready to start an RPF postdoctoral activity no later than 4 years after completing your PhD.
When does it start?
This fellowship’s start date is June 2026 or on availability. Please clearly indicate your preferred starting date in your application documents.
Your application
All applications must include a CV, a publication list and your latest certificates. References are not required at this stage.
We are looking forward to your application!
Do you need further support?
Please have a look at our FAQs, you will find them via https://careers.roche.com/global/en/faq.
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Let’s build a healthier future, together.
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