Postdoc: Cellulose Polymer Modelling & ML MD

University of Manchester; Manchester; Hybrid; GBP 42,000 - 56,000; Full time

Benefits offered by this job

Generous pension scheme

29 days annual leave + holidays

Ride to work / EV car scheme

Job summary

The University of Manchester invites applications for a senior postdoctoral position focused on molecular modelling of cellulose oligomers and self-assembly in the plant cell wall. The role includes day-to-day supervision of PhD/MSc students, project supervision, safety, and manuscript drafting.

You will employ machine learning, molecular dynamics and high-performance computing (Isambard AI) to explore cellulose assembly and screen diverse oligomer structures, contributing to the group’s wider

Qualifications

• PhD in computational chemistry, computational materials science or a relevant discipline.
• Proven research skills demonstrated by publications or projects.
• Experience in molecular modelling of polymers, polysaccharides or cellulose.
• Scientific scripting or programming ability.
• Desirable: molecular modelling and simulation during PhD.
• Desirable: ability to present complex information to varied audiences.
• Desirable: experience manipulating and visualising large datasets.

Responsibilities

• The conducting of high-quality and publishable research in the molecular modelling of cellulose and related materials using advanced simulations and ML techniques on HPC platforms.
• Analysing and interpreting data and making significant input to the project direction.
• Preparation and presentation of regular progress reports.

Skills

Molecular modelling

Polymer dynamics

Programming / scripting

Research skills

Education

PhD in computational chemistry / computational materials science

Job description

The University of Manchester invites applications for a senior postdoctoral position focused on molecular modelling of cellulose oligomers and self-assembly in the plant cell wall. The role includes day-to-day supervision of PhD/MSc students, project supervision, safety, and manuscript drafting.

You will employ machine learning, molecular dynamics and high-performance computing (Isambard AI) to explore cellulose assembly and screen diverse oligomer structures, contributing to the group’s wider