Dual-award between The University of Manchester and the University of Tokyo

Project Description

The matter that we know of constitutes only 15% of our universe - the remaining 85% is an enigmatic form of matter known as dark matter. The focus of this PhD project is to produce and detect this elusive dark matter through collisions of ordinary matter at CERN's Large Hadron Collider, employing novel techniques including AI and quantum algorithms for the computationally intensive task of particle tracking. The student in this project will become a member of ATLAS collaboration at CERN, one of the largest experimental endeavours in history. They will be guided by experts from the Universities of Tokyo and Manchester in data taking, data analysis, and AI/quantum computing. This project coincides with a pivotal period for ATLAS, spanning two years of data collection and two years of preparation for the upcoming accelerator upgrade.

This project has two main goals: searching for dark matter particles in the current dataset while developing algorithms to handle the increased inputs resulting from the accelerator upgrades. The project addresses a critical challenge in detecting subtle dark matter signals in real-time efficiently. New algorithms, integrating as much information as possible from the detector and utilising Machine Learning (ML) methods, will be developed, including forward-looking quantum algorithms. The initial phase concentrates on upgrading the real-time data-taking system, known as the trigger, by implementing a technique called Advanced and Global Particle Flow (GPF) and employing outlier detection techniques to optimise computational costs for particle tracking. This ensures that we don’t discard dark matter signals with unusual detector signatures.

Simultaneously, the student will analyse LHC data for signs of disappearing particle tracks that are the signature of heavy particles decaying into dark matter candidates. Innovative machine learning approaches will be employed to distinguish signals from the vast background noise. Active contribution to a machine-learning-based disappearing track trigger will enhance the student’s skills in particle tracking, physics analysis, and machine learning.

After a year at the University of Tokyo, the student will move to the CERN laboratory in Geneva for a Long Term Attachment. Collaborating with local experts and remotely with their supervisory committee, the student will continue to refine the trigger system, integrating disappearing track triggers into the GPF algorithm and bringing the disappearing track analysis to publication.

In the third year at Manchester, the student will continue working on outlier detection to expand GPF triggers to encompass diverse particle tracks from alternative dark matter scenarios. This effort culminates in the development of a comprehensive GPF trigger system, ready for the upgraded LHC accelerator and ATLAS experiment. Pushing beyond the state-of-the-art, the student will design non-standard tracking algorithms for quantum computers in collaboration with experts from the University of Manchester's Theory group. Quantum improvements to particle tracking position the field for the opportunities presented by quantum computing, and will become part of strategic efforts due to its applicability to other fields. The overall work on fast and efficient ML algorithms for real-time analysis aligns with ongoing efforts toward Net Zero and Global Sustainability Goals in both countries.

Supervisory team email addresses

• Professor Ryu Sawada (University of Tokyo)
• Professor Caterina Doglioni (University of Manchester)

To apply for a PhD in Physics (2024 entry), see an overview of the PHD Physics programme.

Project Description

This project tackles the complex interplay between light, pressure and magnetism, which lies at the heart of modern materials science, through the exploration of light- and pressure-responsive molecular magnets. In the first year, the research team at UTokyo will orient the Ph.D. student to the research environment and the student will conduct an extensive literature review in order to select optimal starting materials. This will set the stage for the preparation and in-depth characterization of new materials, the former of which will include employing advanced spectroscopic techniques and conducting magnetic studies under light irradiation and pressure. 

The groundwork laid in the first year will allow the focus to be narrowed to developing promising research directions in the second year, during which the main research goals will be the identification of materials with superior properties and the collection and preparation of data for theoretical calculations at UoM to facilitate seamless integration of experiments and modelling.

In the third year of their Ph.D. the student will move to UoM to perform theoretical modelling of the metastable states in photomagnetic systems and molecular magnets under high pressure. The literature review and outcomes from the experiments in UTokyo will guide the selection of the most appropriate methods to obtain further insight into the experimental results.

In their final year, the student will focus on finalizing their data analysis and submitting research papers, and on writing up their thesis. The student will continue to be supported with feedback from colleagues and advisors, which will include preparation for the Ph.D. thesis defence. The student will also be supported to explore and apply for post-Ph.D. employment, including potential post-doctoral opportunities or industrial employment, depending on their interest.

Supervisory team email addresses

• Professor Shin-ichi Ohkoshi (University of Tokyo)
• Doctor Jonathan Skelton (University of Manchester)

To apply for a PhD in Chemistry (2024 entry), see an overview of the PHD Chemistry programme.

Project Description

Ecteinascidin 743 (ET-743, Trabectedin) is a marine-derived natural organic compound approved for the treatment of soft tissue sarcomas. A chemically modified synthetic derivative, lurbinectedin, was also approved in 2020 for the treatment of metastatic small cell lung cancer. Consequently, this family of natural products is anticipated to have further applications as pharmaceutical lead compounds. Although ET-743 is biosynthesized from amino acids in microorganisms, only trace amounts can be extracted from biological samples. Hence, chemical synthesis is essential for supply; however, due to structural complexity, these compounds require over twenty steps of chemical transformations and purification. Therefore, establishing a more straightforward and environmentally friendly synthetic approach to expedite the production of the scarce natural products could facilitate the development of such underutilized molecular resource, enabling a better understanding of their pharmacological effects and advancing drug discovery.

This project aims to establish a rapid and customizable process for chemo-enzymatic synthesis and production of ET-743 and its analogues by actively integrating synthetic biology, in vitro enzymatic transformation, and chemical synthesis. At The University of Manchester, a highly automated manufacturing method for microbial metabolic pathways, the Design–Build–Test–Learn pipeline, will be applied to establish a new in vivo production system for amino acid derivatives, biosynthetic intermediates of ET-743, building on the experience collected in the EU-funded H2020 ShikiFactory100 project. This highly automated methodology realizes rapid introduction of genes encoding enzymes catalysing desired reactions, while optimizing native microbial metabolic pathways of microorganisms (e.g. E. coli and Streptomyces) to produce high-value compounds solely through microbial cultivation.

After constructing the in vivo production system for the desired biosynthetic intermediates, enzymes that construct the molecular skeleton of ET-743 will be applied to the production system, accomplishing the one-step construction of the complex core skeleton of ET-743 at UT. Since these enzymes require two substrates (an amino acid derivative and a peptidyl aldehyde), the peptidyl aldehyde will be supplied through chemical synthesis. The project will establish a novel synthetic platform that enables rapid and flexible construction of intricate skeletons from chemically synthesized simple starting materials in a single reaction vessel, integrating the supply of amino acids from engineered microbes and the enzymatic assembly of densely functionalized multicyclic alkaloidal scaffolds. This system will also be adapted to apply enzymes that construct different antitumor complex scaffolds from simple and similar starting materials, aiming to build a library of natural product-like bioactive novel chemical entities.

Through the integration of accumulated knowledge of both research groups at UoM and UT, and the use of advanced experimental equipment, the project will enable students to acquire interdisciplinary knowledge and technologies that enable the synthesis of valuable molecules. This project will contribute to the cultivation of students who possess solid skills capable of excelling globally and broadly in modern society, where the development of greener methods for substance production is urgently needed. 

Supervisory team email addresses

• Professor Hiroki Oguri (University of Tokyo)
• Professor Eriko Takano (University of Manchester)

To apply for a PhD in Chemistry (2024 entry), see an overview of the PHD Chemistry programme.

Project Description

The transition to habitual upright walking is one of the defining features of the human (as opposed to the chimpanzee) lineage and understanding its evolution is therefore of extreme interest to palaeoanthropologists. The aim of the project is to produce a computer simulation of bipedal/quadrupedal locomotion in human ancestors who just began to walk bipedally based on a neuro-musculoskeletal model and reinforcement learning, with the aim of understanding the driving force behind the evolutionary transition from quadrupedal to bipedal locomotion in early hominids. The project combines both experimental studies of non-human primate locomotion in African great apes and cercopithecoid monkeys, and computational simulation studies of bipedal/quadrupedal locomotion using anatomically based neuro-musculoskeletal models of early hominids. An ideal candidate will have a biology/anthropology background with an interest in experimental techniques and good numerical skills and training will be provided in all the necessary techniques.

Supervisory team email addresses

• Professor Naomichi Ogihara (University of Tokyo)
• Professor William Sellers (University of Manchester)

To apply for a PhD in Earth Science (2024 entry), see an overview of the PHD/MPhil Earth Science programme.