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Postdoctoral Researcher - Process Modeling and Optimization for Pharmaceutical Manufacturing

Location: Schaffhausen, Switzerland

Position Summary

We are seeking a highly motivated Postdoctoral Fellow to advance modeling, simulation, and optimization capabilities for pharmaceutical manufacturing processes.

Based in Schaffhausen, Switzerland, the successful candidate will work at the intersection of process science, engineering, manufacturing, and data analytics to develop innovative solutions that improve process understanding, operational performance, and technology deployment across pharmaceutical manufacturing operations.

The role offers the opportunity to apply advanced Process Systems Engineering methodologies to real-world manufacturing challenges while collaborating with multidisciplinary teams across process development, manufacturing, automation, and digital technology.

Key Responsibilities

• Develop mechanistic, hybrid, and data-informed models for pharmaceutical manufacturing processes.
• Design and implement dynamic simulations and digital twins to support process understanding and decision-making.
• Apply Process Systems Engineering methodologies including: Dynamic modeling Parameter estimation Process optimization Sensitivity analysis Uncertainty quantification
• Develop predictive models using experimental and manufacturing data.
• Support process monitoring, process analytics, and operational decision-support capabilities.
• Collaborate with scientists, engineers, and manufacturing teams to translate process knowledge into deployable solutions.
• Contribute to scientific publications, patents, and technology development activities.
• Support identification and evaluation of innovative technologies that enhance manufacturing performance and process robustness.

Required Qualifications

Education

PhD in:

• Chemical Engineering
• Process Systems Engineering
• Pharmaceutical Engineering
• Biochemical Engineering
• Related Engineering Discipline

Technical Experience

Demonstrated expertise in several of the following:

• Dynamic process modeling
• First-principles or mechanistic modeling
• Process simulation
• Mathematical optimization
• Parameter estimation
• Data analytics and statistical modeling
• Scientific programming using Python, MATLAB, Julia, or similar platforms

Preferred Experience

Experience in one or more of:

• Pharmaceutical manufacturing
• Continuous manufacturing
• Process development
• Manufacturing sciences
• Process control
• Process monitoring
• Digital twins
• Advanced process analytics

Ideal Candidate

The ideal candidate combines strong engineering fundamentals with practical experience modeling physical process systems.

Candidates should demonstrate experience developing mathematical models that improve understanding, prediction, optimization, or control of manufacturing processes and be capable of translating scientific concepts into practical industrial applications.

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