Computational Research Scientist – Molecular Simulation & Peptide Design

vVARDIS is a Swiss-based oral health company founded by Dr. Haley and Dr. Goly Abivardi, dentists, innovators, and award-winning entrepreneurs. With a combined 30 years of dental and entrepreneurial experience, and 20 years of laboratory and clinical research, vVARDIS delivers innovative, scientifically proven solutions that enhance oral health and well-being. Our unique technology, supported by over 200 publications, treats early caries noninvasively and pain-free for patients of all ages. We offer oral health products for dental professionals, patients, and consumers, all based on our proprietary technology. Headquartered in Zug, Switzerland, with operations in Europe and the U.S., we foster a collaborative work environment grounded in innovation, excellence, integrity, and empathy.

About the Role

vVARDIS is building an in-house computational capability to accelerate the design of next-generation functional peptides based on our proprietary technology. As a Computational Research Scientist, you will be a founding member of this effort, driving the molecular-simulation half of a closed-loop, computationally guided peptide-design platform. Your models will propose and prioritize novel oligopeptide sequences with tailored physicochemical properties, which are then synthesized and tested experimentally. The resulting data will feed back to refine the next design cycle.

Based at our Michigan dental materials research laboratory and working closely with the parent-company R&D group in Zug, Switzerland, you will collaborate daily with our experimental team to design a tight design–simulate–synthesize–test loop. This role will help lay the scientific and technical foundations of a platform we intend to grow over the coming years. Hybrid arrangements and level/title commensurate with experience are open for discussion.

Responsibilities

• Build and run the molecular-simulation pipeline that proposes and ranks novel oligopeptide sequences against multiple target physicochemical and functional properties relevant to our platform.
• Develop force-field parameterizations and simulation protocols as needed.
• Curate and steward a structured, high-quality dataset linking peptide sequence to measured physicochemical and mineralization behavior, recording both successes and failures; this dataset is the long-term foundation of the design platform.
• Contribute to platform architecture and, over time, to the integration of machine-learning and active-learning approaches (e.g., protein/peptide models, generative sequence design, Bayesian optimization) for multi-objective sequence optimization.
• Communicate methods and findings clearly to a multidisciplinary team, help shape scientific direction and tooling choices, and mentor junior scientists as the team grows.

Qualifications

• PhD in computational chemistry, physical chemistry, biophysics, chemical engineering, or a related field.
• A demonstrated, hands-on track record in biomolecular molecular dynamics, evidenced by peer-reviewed publications.
• Direct experience simulating peptide or protein self-assembly is strongly preferred; a background in a relevant biomolecular self-assembly research group is ideal.

Skills

• Required:
  • 3+ years of hands-on molecular dynamics expertise with at least one standard engine (e.g., GROMACS, AMBER, OpenMM, NAMD, CHARMM, etc.).
  • 2+ years of coarse-grained molecular dynamics for self-assembly (Martini / martinize2 or equivalent), including model setup, adaptation, and validation.
  • Strong scientific programming and workflow automation (Python, Bash) in a Linux / high-performance-computing environment.
  • A solid grasp of peptide and protein biophysics and the physical chemistry of self-assembly, electrostatics, and pH effects.
  • 3+ years of project management experience collaborating with multidisciplinary teams in a leadership role.
  • Aptitude and willingness to acquire additional specialized simulation methods on the job.
  • Working familiarity with biophysical characterization (CD spectroscopy, ThT fluorescence, TEM/AFM, CMC / tensiometry) to help bridge computation and experiment.
  • Excellent communication skills, with the ability to explain complex computational concepts to experimental scientists and non-specialists.
• Preferred:
  • Free-energy and enhanced-sampling methods (e.g., FEP, umbrella sampling, metadynamics).
  • Constant-pH molecular dynamics and pKa / protonation-state modeling.
  • Experience modeling peptide interactions with mineral surfaces (e.g., hydroxyapatite or calcium phosphate) and the associated force fields.
  • Force-field parameterization for non-standard or novel chemistry.
  • Machine learning for molecular systems: protein/peptide language models, generative sequence design, and active learning / Bayesian optimization.
  • Familiarity with commercial modeling suites (e.g., Schrödinger) alongside open-source tools.
  • Scientific curiosity and a passion for solving complex molecular design challenges.