Principal Scientist - In Silico Research & Modeling (f/m/d)

Scientific Leadership & Innovation

• Define and drive Coriolis' scientific strategy for in silico research and modeling technologies.
• Lead the evaluation and implementation of novel computational methodologies relevant to biopharmaceutical development.
• Serve as the internal scientific authority for molecular modeling, simulation, and data-driven predictive technologies.
• Identify emerging technologies and assess their applicability to Coriolis' scientific strategy and service offerings.
• Drive high-impact research initiatives and collaborations that expand Coriolis' capabilities and competitive positioning.
• Establish scientific roadmaps for future digital and computational service offerings.

In Silico Research & Modeling

• Develop and apply advanced molecular modeling and simulation approaches to support drug product development.
• Lead computational investigations of protein stability, aggregation, conformational dynamics, formulation behavior, and molecular interactions.
• Design and execute molecular dynamics (MD) simulations and related computational workflows.
• Utilize advanced modeling approaches including:
  • Atomistic Molecular Dynamics
  • Coarse-Grained Simulations
  • Enhanced Sampling Methods
  • Brownian Dynamics
  • Multiscale Modeling
  • Molecular Docking
  • Free Energy Calculations
  • AI/ML-Assisted Modeling
• Develop predictive models supporting formulation optimization and developability assessments.
• Translate computational insights into actionable recommendations for development teams and clients.

Portfolio Development & Business Impact

• Identify opportunities to expand Coriolis' service portfolio through computational and digital solutions.
• Create novel consulting and service offerings around in silico formulation development and modeling.
• Support strategic business development initiatives through scientific expertise and client-facing engagements.
• Contribute to proposal development and scientific discussions with prospective clients.
• Evaluate potential partnerships with software providers, technology companies, academic groups, and research consortia.

Cross-Functional Collaboration

• Collaborate closely with Formulation Development, Analytical Development, Process Development, and Process Analytics teams.
• Drive integration of computational approaches into laboratory workflows and client programs.
• Facilitate knowledge transfer and adoption of modeling technologies across Coriolis.
• Act as scientific authority for complex client projects requiring advanced computational expertise.

External Scientific Leadership

• Publish scientific findings and contribute to advancing the state-of-the-art in computational biopharmaceutical sciences.
• Establish and maintain collaborations with leading academic institutions and industry partners.
• Build Coriolis' external reputation in digital and computational drug product development.
• Represent Coriolis at scientific conferences, industry forums, and expert networks.

Mentorship & Capability Building

• Mentor scientists and technical experts in computational methodologies.
• Build organizational capabilities in modeling, simulation, and data science.
• Promote scientific excellence and innovation through training and knowledge sharing.

• PhD in Computational Biophysics, Biophysics, Computational Chemistry, Molecular Modeling, Physical Chemistry, Bioengineering, Biotechnology, Pharmaceutical Sciences, Physics, or a related field.
• Minimum 10+ years of relevant industrial and/or academic experience in computational life sciences.
• Recognized expert in Molecular Dynamics and advanced computational modeling with demonstrated impact on biopharmaceutical development programs
• Demonstrated track record of applying computational approaches to biologics, protein science, vaccines, gene therapies, or related modalities.
• Experience driving scientific innovation and translating research into practical applications.
• Established scientific reputation demonstrated through publications, patents, conference presentations, or recognized industry contributions.
• Deep expertise in Molecular Dynamics (MD) simulations.
• Strong knowledge of protein structure, stability, aggregation, and molecular interactions.
• Experience with computational biophysics and molecular modeling methodologies.
• Strong programming skills (Python, R, or similar).
• Experience with high-performance computing environments.

Preferred:

• AI/ML applications in molecular sciences.
• Protein language models and generative AI approaches.
• AlphaFold and structure prediction technologies.
• Schrödinger, GROMACS, AMBER, NAMD, OpenMM, Rosetta, or equivalent platforms.
• Statistical thermodynamics and free-energy methods.
• Digital twins and mechanistic modeling.
• Formulation modeling and developability prediction.
• Multiscale modeling approaches.