Postdoctoral Research Associate in Polymeric Materials Modelling
Role Overview
A senior postdoctoral position focused on molecular modelling of cellulose oligomers and their self-assembly in the plant cell wall.
The research combines machine learning, molecular dynamics simulations, and high-performance computing, including Isambard AI.
The study will screen arrays of cellulose oligomer structures with different moieties and charges to investigate practical assembly.
Key Skills for This Role
Full Job Posting
Research role
A senior postdoctoral position focused on molecular modelling of cellulose oligomers and their self-assembly in the plant cell wall.
The research combines machine learning, molecular dynamics simulations, and high-performance computing, including Isambard AI.
The study will screen arrays of cellulose oligomer structures with different moieties and charges to investigate practical assembly.
Research and project duties
- Conduct high-quality, publishable research on molecular modelling of cellulose and related plant cell-wall materials.
- Implement, execute, and analyze advanced molecular simulations; apply machine-learning techniques and use high-performance computing platforms.
- Contribute to project development and execution, analyze and interpret data, and inform the project's scientific direction.
- Prepare and present regular research progress reports.
- Assist with day-to-day supervision of PhD and MSc students, including project supervision, health and safety, and manuscript drafting.
Essential criteria
- PhD in computational chemistry, computational materials science, or a relevant discipline.
- Proven research skills and scientific scripting or programming.
- Experience in molecular dynamics simulations of polymers.
- Applicants are asked for experience modelling polymers and polysaccharides; cellulose experience is preferred.
Desirable criteria
- Molecular modelling and simulation experience during a PhD is desirable.
- Ability to present complex information effectively to different audiences is desirable.
- Experience manipulating and visualizing large datasets is desirable.
Benefits
- Benefits include an employer-contribution pension, 29 days of annual leave plus bank holidays and Christmas closure, and ride-to-work and electric-vehicle car schemes.
- The university provides information about flexible and hybrid working; no specific working arrangement is stated for this role.
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