Support the ecosystem of digital chemistry, including services, applications and infrastructure that accelerate product development tasks within Reckitt.
Responsibilities
Deliver computational chemistry and molecular modelling analyses
Use and manage HPC environments
Collaborate across Digital Science & AI, R&D, IT&D and external partners
Translate scientific questions into modelling workflows
Combine computational predictions with experimental data
Requirements
Bachelor’s, Master’s or PhD in Chemistry, Computational Chemistry, Biochemistry or a relevant field
The candidate will participate in molecular modeling and/or cheminformatics research, either in an independent project or as part of a drug discovery team, learning how to employ data, models, and methods to drive hypothesis-driven drug discovery.
Senior expert scientist responsible for applying computational and experimental approaches to improve product understanding, formulation development, stability assessment, and development decisions for biologic medicines.
Compensation
Salary Range: 46.550,00 € - 86.450,00 €
Responsibilities
Independently evaluate and interpret complex scientific data, draw conclusions, identify risks and opportunities, and provide recommendations to support project decisions.
Develop and apply advanced modeling, data science, and biophysical approaches to strengthen product understanding and support formulation, stability, and drug product development.
Lead the development, optimization, and implementation of new scientific approaches, models, workflows, and technologies and ensure their effective application within development projects.
Provide scientific and technical guidance across project teams, proactively troubleshoot complex problems, and communicate recommendations effectively across organizational interfaces.
Drive knowledge sharing and capability building, including transfer of methods and know-how to other departments, sites, and external partners.
Requirements
PhD in Biochemistry, Chemistry, Biophysics, Physics, Computational biology, Pharmaceutical sciences, or similar
Senior expert in biologics development, advanced biophysical characterization, data science and molecular modeling, responsible for computational and experimental approaches to improve product understanding, formulation development, stability assessment and support development decisions for biologic medicines.
Compensation
Salary Range: 46.550,00 € - 86.450,00 €
Responsibilities
Independently evaluate and interpret complex scientific data
Develop and apply advanced modeling, data science, and biophysical approaches
Lead the development, optimization, and implementation of new scientific approaches, models, workflows, and technologies
Provide scientific and technical guidance across project teams
Drive knowledge sharing and capability building
Requirements
PhD in Biochemistry, Chemistry, Biophysics, Physics, Computational biology, Pharmaceutical sciences, or similar
Uncover the molecular mechanisms governing adhesive composite interfaces and advance atomistic simulation approaches for the development of sustainable, thermally debondable adhesives.
Compensation
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
Responsibilities
Perform Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate adhesive–composite interfacial interactions, calculate adhesion energies, and characterise interfacial structure and wetting behaviour for different adhesive molecules.
Investigate the molecular mechanisms governing adhesion and interfacial stability, examining how adhesive chemistry and molecular structure influence binding strength and the thermodynamic stability of the adhesive–composite interface.
Perform tensile and shear deformation simulations to evaluate the mechanical performance of adhesive–composite interfaces, identify failure mechanisms and investigate how molecular interactions influence resistance to interfacial separation.
Assess the suitability of existing interatomic potentials for modelling adhesive–composite interfaces by benchmarking their predictions against DFT calculations and, where necessary, develop machine-learning interatomic potentials (MLIPs) to enable accurate and efficient atomistic simulations.
Collaborate closely with experimental researchers and project partners to connect atomistic insights with experimental observations and support the development of durable and thermally debondable adhesives.
Lead computational chemistry efforts to design and model oncology-targeted therapeutics, integrating AI/ML and structure-based approaches.
Compensation
The annual base pay for this position ranges from 124,713.60 - 187,070.40 USD Annual [80% - 120% ]. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
Responsibilities
Generate testable design hypotheses integrating structural insights, SAR, and biophysical data
Apply docking, pharmacophore modeling, and data-driven design to prioritize compounds
Build, validate, and deploy predictive AI/ML models for high-throughput experiment design
Develop computational methods for antibody–drug conjugates and proximity-inducing agents
Partner with medicinal chemistry, biology, biophysics, DMPK, and safety sciences
Harness structure-based design, ligand-based approaches, and modern AI/ML to guide high-impact discovery programs across new modalities in oncology targeted discovery.
Compensation
The annual base pay for this position ranges from 124,713.60 - 187,070.40 USD Annual [80% - 120% ]
Responsibilities
Generate testable design hypotheses, integrating structural insights, SAR, and biophysical data to guide medicinal chemistry and accelerate decision-making.
Apply docking, pharmacophore modeling, and data-driven design to prioritize compounds that improve potency, selectivity, and developability.
Build, validate, and deploy predictive models using contemporary toolkits to support high-throughput experiment design and increase hit-to-lead success.
Develop and refine computational methods for antibody–drug conjugates and bifunctional, proximity-inducing agents, expanding design capabilities beyond small molecules.
Partner closely with medicinal chemistry, biology, biophysics, DMPK, and safety sciences to align models with mechanistic understanding and project goals.
Requirements
Ph.D. in computational chemistry or a related field
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