Postdoc and PhD openings become available on a rolling basis. Informal inquiries via email are welcome.
Start dates and topics are flexible. Topics are in the general areas of:
- Development of nonadiabatic and approximate quantum dynamics methods for condensed phase systems
- Machine learning of electronic structure
- Simulation of ultrafast dynamics at surfaces and light-driven catalysis
Specific current opportunities:
A fully funded 4-year PhD project in Machine-learning-guided design of efficient sustainable materials for hydrogenation photocatalysis (flexible 2023-2024 start date)
The project is open to candidates with a science Bachelor/Master degree (Chemistry, Physics, Mathematics, Computer Science) and covers 4-year stipends including tuition fees. Successful candidates will become members of the interdisciplinary Computational Surface Science group (www.warwick.ac.uk/maurergroup) led by Prof. Reinhard Maurer based in the Departments of Physics and Chemistry at the University of Warwick, UK.
The Scientific Mission: Fuel cells, photovoltaic devices, photocatalytic converters – they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale within the coming decades. They all fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact to deliver a certain function. The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport. Molecular simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in the physical sciences, things are rapidly changing. This project is part of a large initiative that aims to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate ultrafast chemical dynamics at surfaces and in materials.
Training: Successful candidates will join a large, interdisciplinary research group that provides a collaborative and supportive environment. Projects will often involve teamwork and joint problem solving between colleagues with complementary skills. The successful candidate will be trained in state-of-the-art machine learning methodology, electronic structure theory, and molecular simulation methods. The student will acquire important transferable skills such as software development (Python, Julia) and project management. Substantial resources are available for group members to attend international workshops and conferences. The project is designed to balance method development and application simulation efforts – the latter will include close collaboration with experimental project partners.
The Project: In this project, you will use newly developed machine learning surrogate models to simulate the light-driven promotion of CO hydrogenation to CHO on plasmonic catalyst materials, an important bottleneck reaction in syngas and CO2 reforming. The PhD project will establish the mechanistic details of hot electron interaction with CO and the key design parameters for optimal photocatalytic CO hydrogenation. You will contribute to the development of broadly applicable electronic structure methods and machine learning methods with the specific goal to accelerate the screening of optimal photocatalyst materials.
Interested candidates should contact Prof. Reinhard Maurer (email@example.com) with their CV and a motivation statement. Details on the formal application procedure can be found at https://warwick.ac.uk/study/postgraduate/apply/research/.
A fully funded 4-year PhD project in Computational design of topological defects in graphene (flexible 2023-2024 start date)
A fully funded 4-year PhD project in “Computational design of topological defects in graphene” is available with a flexible 2023-2024 start date. The project is open to candidates with a science Bachelor/Master’s degree (Physics, Chemistry, Materials Science) and includes a 4-year stipend with tuition fees. Successful candidates will become members of the interdisciplinary Computational Surface Science group (www.warwick.ac.uk/maurergroup) led by Prof. Reinhard Maurer based in the Departments of Physics and Chemistry at the University of Warwick, UK.
The nanomaterial graphene is ultrathin, ultra-strong and highly conductive. However, to find new real-world applications it must be tailored to a given function, e.g. to be more adhesive or to sense gases. Idealised graphene is a two-dimensional sheet of pure carbon. Its topology is defined by linked hexagonal rings of carbon; each hexagon perfectly identical to the next. By topologically designing defects to build graphene block by block with non-hexagonal rings, non-carbon atoms, or missing carbon atoms, we can controllably introduce new functionality to graphene that will enhance its applicability to support metal catalysts, to sense molecules, and to be used as components in nanoelectronic devices.
The Project: The goal of this PhD project is to develop and employ computational simulation and electronic structure theory approaches to identify how certain local defects can be created in two-dimensional graphene networks. The simulations will be done in close collaboration with experiment, in fact, they will directly inform which experiments will be conducted. We will aim to identify general design principles of local topological defect formation to identify which defects can be preserved in networks and which ones cannot. The project will also involve method and software development to enable large-scale simulations on high performance computing architectures such as the UK national supercomputer. Where surface superstructure models become too large for direct quantum mechanical calculations, acceleration techniques based on machine-learning methods will be employed. Upon successful completion of the computational screening and characterisation of defective graphene networks, we will study the ability of the grown networks to support new types of metal catalyst materials and nanostructured magnetic materials.
The studentship is part of a collaborative project funded by the Leverhulme Trust and will feature close collaboration with experimental project partners who synthesize organic molecular precursors and who perform surface synthesis and characterization of defective graphene. The student will work closely with experimental team members to directly inform which experiments will be conducted and to provide computational simulation support for the characterization of the structure and spectroscopic properties of experimentally fabricated two-dimensional networks.
Successful candidates will join a large, interdisciplinary research group that provides a collaborative and supportive environment. The PhD student will be trained in state-of-the-art electronic structure theory, molecular simulation methods, and machine learning methods, all of which are well established in the host group. The student will acquire important transferable skills such as software development (Python) and project management and present their research at international and national conferences.
Interested candidates should contact Prof. Reinhard Maurer (firstname.lastname@example.org).
A fully funded 4-year PhD project in property-driven generative machine learning for tailored materials design (flexible 2023-2024 start date)
A fully funded 4-year PhD project in property-driven generative machine learning for tailored materials design is available with a flexible 2023-2024 start date. The project is open to candidates with a science Bachelor/Master degree (Chemistry, Physics, Computer Science) and includes a 4-year stipend with full tuition fees. Successful candidates will become members of the interdisciplinary computational research group led by Prof. Reinhard Maurer (www.warwick.ac.uk/maurergroup) based in the Departments of Physics and Chemistry at the University of Warwick, UK.
When chemists search for new functional molecules with tailored properties, they synthetically modify known structural motifs of molecules by trial and error. This empirical exploration of the space of possible chemical compounds forms the present-day bread-and-butter business of chemical innovation. In the last few decades, quantum chemistry has further aided this process by accurately predicting properties in silico, based on formal quantum theory, if provided with molecular structures and significant computational resources. However, certain applications require molecules to satisfy specific properties, for example the emission of light of a specific colour for novel organic light-emitting diode (OLED) materials. Here it might be secondary what the structure of the molecule is as long as it is easy to synthesise. Trial-and-error based on structural modification is unlikely to identify the best and most efficient material; at best, it would be very time consuming. Machine learning methods are ideal to address this inverse property-driven design task. Generative deep learning models are extremely powerful for image generation (deep fake images) and large language models (e.g. ChatGPT). They can also be used to generate new molecules.
The Project: The goal of this PhD project is to develop generative machine learning models that can create new undiscovered molecules with tailormade electronic and optical properties for specific applications in OLED materials, organic solar cells, and quantum sensing applications. The student will use existing and generate new data to train generative machine learning models and use advanced techniques to condition and bias such models so that the model predicts molecules with specific narrow properties such as light emission properties or ease of synthesizability. The project will also explore how this approach can be extended to more complex materials, such as molecular crystals, surface nanostructures, and metal nanoparticles.
Successful candidates will join a large, interdisciplinary research group that provides a collaborative and supportive environment. Projects will often involve teamwork and joint problem solving between colleagues with complementary skills. The successful candidate will be trained in state-of-the-art machine learning methodology, electronic structure theory, and molecular simulation methods. The student will acquire important transferable skills such as software development and project management. Substantial resources are available for group members to attend international workshops and conferences.
Interested candidates should contact Prof. Reinhard Maurer (email@example.com).
Three-year fixed-term PostDoc position as Research Fellow in Machine Learning in Chemical Physics available
We are looking for a postdoctoral researcher to join a project on “Machine learning of electronic structure for chemical dynamics” in the group of Prof. Reinhard Maurer in the Department of Chemistry and the Department of Physics at the University of Warwick. In this role you will work on a curiosity-driven project, towards developing novel machine learning representations of electronic structure to enable hitherto unfeasible molecular and quantum dynamics simulations. By combining machine-learning methods, nonadiabatic dynamics methods, and Density Functional Theory, the project will develop a first principles-based simulation framework for the description of light and electron-driven reaction dynamics in heterogeneous catalysis and photoelectrocatalysis.
The project takes place in a large and vibrant interdisciplinary research group and will provide you with space and resources for your personal career and profile development, combining method development, application to real-world problems, and collaboration with experimental groups. Your project will complement ongoing work by other members in the group working towards common goals. The contract is for a fixed term of 36 months.
Who we are: In the Maurer group at Warwick, we aim to develop computational simulation methodology to study quantum phenomena at surfaces with applications ranging from plasmonic catalysis, to nanotechnology, and electrochemistry. Our goal is to combine electronic structure theory, molecular and quantum dynamics methodology, and machine learning methods to achieve an accurate yet computationally feasible description of complex phenomena at solid/gas and solid/liquid interfaces. The currently 15 members of the group come from diverse backgrounds in chemistry, mathematics, and physics, and foster a collaborative and supportive work environment. The group is one of seven computational chemistry groups and part of an interdepartmental computational materials research community spanning the Chemistry, Physics, and Engineering departments.
Who you are: You might have a background in computational chemistry/physics, theoretical condensed matter physics, computer science, or a similar field. You love working on hard problems that live at the boundary of theoretical method and computational software development. You enjoy working in a team where you contribute your expertise and skill set to deliver an ambitious research vision and where you can contribute to the training of PhD and Master’s students. You are excited about opportunities to communicate with international collaborators across disciplinary and cultural boundaries.
Application Deadline: 05.09.2023
Please apply via the official advert:
Feel free to send your CV and motivation statement to firstname.lastname@example.org
Applications from students interested in research at the Master's, PhD and PostDoc level are always welcome.