A Four Year PhD Position in Data-enhanced Quantum Chemistry for Predictive Catalyst Design

About the Project

Tax-Free Stipend of €25,000 per annum + Academic Tuition Fees (all nationalities) for four years.

Anticipated start date September 2026 or after (not later than March 2027)

A PhD position is available in the School of Chemistry at Trinity College Dublin in the research group of Prof. Tobias Krämer supported by a Trinity Research Doctorate Award (TRDA). The successful candidate will work on an exciting project involving the theoretical study of main group catalysts for small molecule activation.

Background:

Catalysts are essential to modern chemical manufacturing, yet most industrial catalysts depend on scarce and expensive precious metals such as platinum and rhodium, whose extraction carries significant environmental costs. With global catalyst demand continuing to rise, developing sustainable alternatives is a critical challenge for achieving climate goals. This PhD project will investigate the electronic structure and reactivity of Group 13 compounds, focusing on key catalytic processes, such as small-molecule activation and photochemically driven transformations. Complexes based on abundant main-group elements such as aluminium and gallium have recently shown an unexpected ability to activate chemical bonds traditionally associated with precious metals. The research will combine state-of-the-art quantum chemical simulations with machine learning to understand catalyst behaviour, predict promising new catalyst designs, and accelerate discovery beyond traditional trial-and-error approaches. The project will explore innovative concepts including cooperative multi-metal systems and light-driven reactivity, while working closely with experimental collaborators to validate computational predictions in the laboratory. By advancing sustainable catalyst technologies, this research will contribute to the development of greener chemical processes and support global efforts toward sustainable industry and climate action.

The ideal candidate will demonstrate an interest in applying computational and data-driven methods to address major sustainability challenges in chemistry and catalysis. Candidates with strong backgrounds in computational modelling and electronic structure and a strong interest in machine learning applied to chemical systems are encouraged to apply.

Prior experience in scientific programming, high-performance computing environments, and computational workflow development is desirable. The successful applicant will be motivated to develop expertise at the interface of quantum chemistry, machine learning, and catalyst discovery, contributing to the creation of predictive tools that accelerate the development of sustainable catalytic technologies. Experience collaborating across disciplines and engaging with both theoretical and experimental researchers would be highly beneficial.

The successful candidate will join a growing research environment with access to excellent facilities and the opportunity to engage with the wider molecular modelling community within Trinity College Dublin. The project will involve collaboration with synthetic chemists, with opportunities for conference participation, publications and public engagement and outreach.

Standard Duties and Responsibilities of the Post

• Conduct original doctoral research in computational chemistry, quantum chemistry, and machine learning associated with the project.
• Perform density functional theory (DFT), ab initio, and related electronic structure calculations to investigate reaction mechanisms and catalytic cycles.
• Analyse electronic structure and bonding using techniques such as Energy Decomposition Analysis (EDA), Quantum Theory of Atoms in Molecules (QTAIM), and related methodologies.
• Construct, curate, and manage computational datasets for machine learning model development and validation.
• Develop supervised machine learning models, including neural network and kernel-based approaches, for predicting catalyst properties and reactivity.
• Identify structure–activity relationships and translate computational insights into catalyst design principles.
• Collaborate closely with experimental researchers to validate computational predictions and support theory-guided synthesis efforts.
• Analyse, interpret and disseminate research findings through publications, technical reports and presentations at national and international conferences, workshops, and scientific seminars.
• Contribute to the supervision and mentoring of MSc, undergraduate and visiting research students within the group.
• Support the day-to-day operation of the group, including maintaining a safe, collaborative, and inclusive research environment.
• Undertake other duties broadly aligned with the objectives and development of the research programme.

Essential Requirements:

The successful candidate will have:

• A first or upper second class honours degree in Chemistry or a closely related discipline.
• A Masters Degree in a relevant discipline would be a plus, but not essential.
• A background in computational chemistry, theoretical chemistry, machine learning or artificial intelligence or related areas would be beneficial.
• A passion for sustainability and the scientific pursuit.

Desirable:

• Experience with electronic structure calculations using software such as Gaussian, ORCA, or equivalent packages.
• Experience with common electronic structure analysis tools (e.g. QTAIM, NBO, PIO)
• Familiarity with density functional theory, ab initio methods, molecular dynamics, or reaction mechanism analysis.
• Knowledge of machine learning techniques and their application to scientific problems.
• Experience handling large datasets and developing predictive computational models.
• Prior research experience in catalysis, organometallic chemistry, main group chemistry, or materials modelling.
• Evidence of scientific writing, conference presentations, or peer-reviewed publications.
• Experience with scientific programming.

Skills & Competencies

• Demonstrated problem-solving and analytical skills, including data analysis.
• Ability to work independently and as part of a collaborative team.
• Good organisational skills and attention to detail.
• Strong written and verbal communication skills.
• Ability to manage and prioritise multiple aspects of an interdisciplinary research project.
• Enthusiasm for developing computational approaches and learning new methods.
• Commitment to responsive, inclusive and safe research practices.
• Ability to contribute to the wider research culture of the group and School.
• Experience with scientific programming and data analysis using languages such as Python, C++, or similar.
• Strong written and verbal communication skills, evidenced through conference presentations, or peer-reviewed publications.

Application Procedure

Applicants should submit:

• a cover letter outlining relevant research experience and motivation for applying
• a full curriculum vitae
• relevant transcripts
• contact details of two academic referees

Please send the application to:

Prof. Tobias Kraemer
kraemert@tcd.ie
with the subject line: “TRDA PhD Application”. Informal enquiries are welcome. The deadline for submission is noon on Friday the 17th of July 2026.