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Up Directory CCL 24.03.17 PhD Studentship in MOLECULAR SIMULATION-GUIDED ENGINEERING OF PROTEIN-SEMICONDUCTOR INTERFACES FOR SEMIARTIFICIAL PHOTOSYNTHESIS
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Date: Sun Mar 17 16:58:39 2024
Subject: 24.03.17 PhD Studentship in MOLECULAR SIMULATION-GUIDED ENGINEERING OF PROTEIN-SEMICONDUCTOR INTERFACES FOR SEMIARTIFICIAL PHOTOSYNTHESIS
Supervisors: Prof. Jochen Blumberger (UCL), Dr. Michael Sullivan 
(A*STAR Institute of High Performance Computing, Singapore)

Application deadline: 15th April 2024  

Start date: 23 September 2024

Location: London (1.5 years), Singapore (2 years)

Eligibility: UK nationals, EU nationals with settled/pre-settled status and 
those with indefinite leave to remain or enter. Nationals of other countries
are not eligible for this studentship due to restrictions by the funder.

Subject areas: Molecular simulation, Neural network potentials, Electron Transfer, Oxide materials, Dyes, (Time-dependent)Density functional theory 

The Studentship:

This position is fully funded by the UCL-A*STAR Collaborative Programme via 
the Centre for Doctoral Training in Molecular Modelling and Materials 
Science (M3S CDT) at UCL. The student will be registered for a PhD at UCL 
where he/she will spend year 1 followed by second and third years of the 
PhD at the Materials Science and Chemistry Department at the A*STAR 
Institute of High Performance Computing in Singapore, then return to UCL 
for the final six months to write up the thesis. 

The project: 
The sustainable production of net-zero fuels is one of the most pressing 
issues of our generation. Artificial photosynthesis, the reduction of 
CO2 to fuel molecules using the energy of the sun and synthetic catalysts 
remains a formidable challenge due to sluggish oxidation kinetics and 
mismatch of time scales between the lifetime of photo-excited states 
and the chemical reactions. In this project, we explore a novel route to 
address these challenges, semiartificial photosynthesis. The aim of this 
PhD project is to develop and apply molecular modelling approaches that 
guide the design of functional interfaces between redox proteins and 
dye-sensitised inorganic semiconductor (SC) nanoparticles, 
Protein-Dye|SC. This involves the development of machine learning/neural 
network potentials for molecular dynamics simulation of the dye molecule 
chemisorbed on the SC, computation of the rates for electron tunnelling 
across the Protein-Dye|SC interface and computer-guided engineering of 
Protein-Dye|SC assemblies with increased lifetime of the fuel-forming 
charge-separated state well beyond the millisecond time scale. 
The project is embedded in a multidisciplinary collaboration with 
experimental teams at University of East Anglia and the University 
of Cambridge who will assemble the computationally designed interfaces 
to semiartificial photosynthesis modules.   

The candidate
 
The applicants should have, or be expecting to achieve, a first or 
upper second-class integrated masters degree (MSci, MChem, etc.) or 
2:1 minimum BSc plus stand-alone Masters degree with at least a Merit 
in Physics, Materials Science, Chemistry or related disciplines. 
The successful applicant will demonstrate good knowledge in quantum 
mechanics and statistical mechanics, interest in writing computer 
code (e.g. python or fortran) and the ability to think analytically 
and creatively. Some experience with molecular simulation and/or 
density functional theory calculations is a plus but not required. 
Previous research experience in contributing to a collaborative 
interdisciplinary research environment is highly desirable but not 
necessary as training will be provided.
Interested candidates should initially contact the UCL-based 
supervisor Prof. Jochen Blumberger by sending an email to 
j.blumberger^ucl.ac.uk, with a CV, degree transcript and a motivation 
letter expressing interest in the project attached to the email. 
Applications are welcome from UK nationals, EU nationals with 
settled/pre-settled status and those with indefinite leave to 
remain or enter.

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Modified: Sun Mar 17 20:58:39 2024 GMT
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