PostDoc in Computational and Experimental Protein Design(m/w/x)

Anforderungen

• Solid understanding of protein biophysics
• Hands-on experience with core laboratory techniques for creating protein variants, such as protein expression and purification, or eagerness to learn
• PhD in a relevant field, such as Computational Structural Biology, Bioinformatics, Biophysics, Biochemistry, or a related field with a strong computational focus
• Deep understanding of machine learning approaches for protein structure and property prediction, and generative models
• Familiarity with structural modeling and design platforms for biomolecules and protein-protein interactions (e.g. MOE, Schrödinger, Rosetta, modeling and docking algorithms)
• Fluency in English and strong oral and written communication skills
• 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)
• Engagement in scientific research continuously since PhD and readiness to start an RPF postdoctoral activity no later than 4 years after completing PhD

Aufgaben

• Develop and validate novel computational methods for antibody engineering
• Integrate Machine Learning with Molecular Dynamics workflows
• Implement a lab-in-the-loop approach for computational modeling and experimental validation
• Create a predictor for antibody affinity maturation and developability
• Contribute to drug discovery efforts for breakthrough medicines
• Attend and present at scientific meetings
• Interact with the scientific community and publish experimental advances
• Collaborate with host teams and stakeholders on protein design algorithms

Berufserfahrung

Ausbildung

Sprachen

Tools & Technologien