Postdoc: AI-Driven Design of Bio-Based Adhesives

SLU

Uppsala kommun

On-site

SEK 420,000 - 540,000

Full time

14 days+
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Job summary

SLU invites applications for a 24‑month postdoctoral position in the Department of Forest Bioeconomy and Technology, Uppsala, to advance AI‑driven design and modelling of bio‑based adhesives for furniture, construction and packaging. You will build data‑driven frameworks linking chemistry to performance, integrating experiments, characterization and machine learning.

The candidate will collaborate with academic and industrial partners to validate formulations and develop a transferable

Qualifications

  • PhD within last 3 years in materials science, polymer science, chemistry, chemical engineering or closely related field.
  • Strong expertise in polymer formulation and structure–property relationships for bio-based adhesives.
  • Experience with advanced material characterization techniques.
  • Interest in data‑driven research and machine learning for materials systems.

Responsibilities

  • Develop data‑driven frameworks linking formulation chemistry to adhesive performance.
  • Integrate experimental formulation, molecular characterization and machine learning to build predictive models.
  • Create a structured formulation library and map key performance indicators.
  • Collaborate with academic and industrial partners to validate formulations on relevant substrates.

Skills

Polymer formulation
Structure-property relationships
Data-driven research
Machine learning
Experimentation
Communication in English
Interdisciplinary collaboration

Education

Doctoral degree (within last 3 years)
Materials science / polymer science / chemistry / chemical engineering

Tools

Advanced material characterization
Modelling approaches

Job description

SLU invites applications for a 24‑month postdoctoral position in the Department of Forest Bioeconomy and Technology, Uppsala, to advance AI‑driven design and modelling of bio‑based adhesives for furniture, construction and packaging. You will build data‑driven frameworks linking chemistry to performance, integrating experiments, characterization and machine learning.

The candidate will collaborate with academic and industrial partners to validate formulations and develop a transferable

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