Organisation/Company Maynooth University Department Department of Chemistry Research Field Engineering » Chemical engineering Chemistry » Computational chemistry Engineering » Other Physics » Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Ireland Application Deadline 20 Feb *********:59 (Europe/Dublin) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme?
Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
The Multiscale Materials Modelling Group at Maynooth University invites applications for three fully funded PhD positions focused on the design of advanced polymer-hybrid anion-exchange membranes using multiscale modelling, data-driven approaches, and experimental validation.
The project combines computational materials science with practical membrane design for electrochemical energy applications.
This PhD position focuses on molecular-scale computational modelling of polymer-hybrid anion-exchange membranes.
The project involves DFT, molecular dynamics, and Monte Carlo simulations to study ion transport mechanisms, interfacial chemistry, and membrane stability.
The researcher will develop structure–property relationships and work closely with experimental partners to validate modelling predictions.
Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage.
PhD Position 2 – Coarse-Grained and Mesoscale Modelling
This PhD position focuses on coarse-grained and mesoscale modelling of polymer-hybrid anion-exchange membranes to understand morphology, phase behaviour, and transport pathways.
The project includes developing coarse-grained models, analysing membrane structure–performance relationships, and integrating simulation results with experimental observations.
Experience or a strong interest in polymer, composites, or soft-matter modelling, scientific programming, and machine-learning-supported optimisation is an advantage.
This PhD position focuses on integrating computational modelling with experimental validation to develop polymer-hybrid anion-exchange membranes.
The project involves translating modelling insights into membrane fabrication strategies, supporting experimental characterisation and electrochemical testing in collaboration with project partners, and feeding experimental results back into modelling and machine-learning-assisted optimisation workflows.
A background or strong interest in materials science, electrochemistry, or membrane characterisation, combined with computational or data-driven skills, is an advantage.
Job Profile
Master's degree in Chemical Engineering, Computational Chemistry, Materials Engineering, Physics, or an equivalent degree.
Demonstrated knowledge in computational (and/or experimental) material science, physics, chemistry, or equivalent experience.
Demonstrated experience with coding (MATLAB, Perl, Python, Jupyter, C, etc.) and quantum chemistry software (VASP, CP2K, quantum espresso, wien2k, Gaussian, etc.), force-field-based simulations software (LAMMPS, DL_MESO, etc), and Monte Carlo methods (self-programming or using software).
Experience or strong interest in data-driven modelling and machine-learning approaches for materials science (e.g., regression, classification, surrogate models, or force-field parametrization using platforms such as PyTorch, TensorFlow, scikit-learn, or equivalent frameworks) is an advantage.
Excellent research, analytical, and scientific writing skills.
Strong oral communication and interpersonal skills, and the ability to represent the research team effectively internally and externally, including presenting research outcomes at national and international conferences.
Motivation, ambition, resilience, and willingness to learn within a multidisciplinary project and research environment.
What We Offer
A fully
funded 4-year
PhD position, including a competitive stipend and full tuition fee coverage.
The successful candidate will be embedded in the Department of Chemistry at Maynooth University, a multidisciplinary research environment with multiple principal investigators and active research groups.
All PhD positions offer strong opportunities for interdisciplinary collaboration, advanced training in computational and experimental materials science, and access to national and international high-performance computing and research infrastructure.
PhD researchers will be supported to attend international conferences, specialist workshops, tutorials, and research stays with project collaborators.
How to Apply
We intend to fill this position as soon as possible, preferably in or before March ****.
Interested candidates can send their curriculum vitae, cover letter, and copies of relevant diplomas and ******, specifying the PhD position you are applying for explicitly in capitals in the subject line of your email (e.g., PHD1 APPLICATION, PHD2 APPLICATION, and PHD3 APPLICATION).
Considered candidates will be contacted shortly thereafter and will be asked for further details.
Updates on the hiring procedure will be provided during the interviews.
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