Postdoc in AI-Driven Design and Modelling of Bio-Based Adhesives
Core
Develop predictive frameworks and high-performance bio-based adhesive systems for furniture, construction, and packaging industries using AI-driven modelling and polymer science.
Role type
Postdoctoral researcher (research IC)
Builds
Predictive models linking chemical descriptors to adhesive properties; structured formulation libraries; data-driven optimization strategies for sustainable materials.
Domain
Materials science / Polymer chemistry / Bioeconomy / Sustainable industry
Deliverable
production ML models | research
Required skills
Polymer formulation and structure-property relationships; modification and crosslinking of bio-based or functional polymers; advanced material characterization techniques; data-driven research methodology; machine learning applied to materials or chemical systems.
Preferred skills
Experience with modelling approaches for materials design; interdisciplinary collaboration in international research environments.
Technologies
Machine learning frameworks; data analysis tools; molecular-level characterization equipment.
Responsibilities
Integrate experimental formulation with machine learning to develop predictive models; map key performance indicators for bio-adhesives; implement data-driven optimization strategies to identify high-performing systems; validate selected formulations on industrially relevant substrates; collaborate with academic and industrial partners to develop a transferable predictive toolbox.
Seniority
Postdoctoral, independent researcher with mentorship potential