Distributed Algorithms CDT
12 Apr 2019
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Funded by EPSRC, the Distributed Algorithms CDT offers studentships at the University of Liverpool with joint supervision from the Hartree Centre.

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At the Hartree Centre, we apply transformative advanced computing, data analytics and AI technologies to industry-relevant challenges, and this is reflected in the projects we're co-supervising with the University of Liverpool as part of the Distributed Algorithms CDT​

Through each of the​​ funded projects below, we hope to provide you with an exciting and engaging area of cutting-edge computational science to immerse yourself in, whilst also delivering essential training in sought-after technical skills that are valued by both science and industry, setting you up for future career success. ​​​​


​​​​​Available Projects

​​​​Machine Learning to Identify Unique Events in Sparse Hyperspectral Datasets 

Supervisors: 

Dr. Yalin Zheng | University of Liverpool

Dr. Jony Castagna | STFC Hartree Centre​



​You will investigate machine learning approaches that may permit images from stable materials to be used to increase the ability of methods such as hyperspectral imaging in electron microscopy and X-ray systems to observe thermodynamically unstable materials and processes on the atomic scale. Such advances have the potential to significantly impact the search for new personalised medicines, the development of new advanced energy storage systems, and our ability to directly see chemistry important for catalysing environmentally friendly processes. 


Learning to See More: Better Bayesian Track Before Detect using Statistical Machine Learning​

Supervisors: 

Dr. Angel Garcia-Fernandez | University of Liverpool

Prof. Vassil Alexandrov | STFC Hartree Centre



​In this project you will aim to improve the ability to detect faint objects in, for example, radar data. By improving detection performance, cheaper sensors will be able to emulate more expensive sensors, making the development of advanced detection algorithms very important in industrial settings. The project is co-created & co-funded by aerospace ​sensor manufacturer Leonardo. ​



​Fast Emulation of Fluid Dynamics​

Supervisors: 

Prof. Leszek Gasieniec | University of Liverpool

Dr. Luke Mason | STFC Hartree Centre




This project is focused on using machine learning to emulate computationally expensive calculations. The emulator can then be used to answer pertinent questions that are impossible to answer otherwise. The aim is to mirror successes that have been achieved using similar approaches in, for example, chemical formula formulation (where a 66% reduction in compute required has been reported) and drug discovery (where a 95% reduction in the compute requirement needed for a certain objective has been reported). This project has been co-defined with and will be co-supervised by 
DSTL

View all available projects


Application deadline for all projects: 31​ Jan 2020​


​​​​​​​Meet the supervisors


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Prof. Vassil Alexandrov

I've worked in high performance computing (HPC), data and computational science for a long time, with a fulfilling career spanning 18 years and 5 countries! I've also published over 130 papers in journals and at international conferences and workshops. I'm excited to be a supervisor so that I can pass on my knowledge and experience to the next generation of young people who will develop research projects in exciting areas of HPC and data science.

During my career, I have supervised 31 PhD students to successful completion of their PhD studies across a variety of computational themes and areas, and been a Programme Director of 3 MSc programmes. I am a member of the Editorial Board of the Journal of Computational Science (JOCS) and Editor of Mathematics and Computers in Simulation journal.

My long-term expertise in Monte Carlo means I am particularly interested in seeing how we can further speed up these simulations. Currently, mathematics-led innovation is clearly indispensable in advancing key scientific areas, as well as powering methods and algorithms enabling to discover global properties of data.


Dr. Xiaohu Guo 

I've been interested in science in my whole life, especially computational science, and can see that it is turning into more and more powerful tool for scientists to explore exciting new areas and unknowns. What excites me about computational science is the great sense of achievement I feel when the algorithms/methods that were turned into codes by my logic and own creativity can be used by every one! What I love about working here is bridging a gap between research academics and industrial applications.

I develop enabling technologies for a wide range of engineering and science applications. I was the lead developer of Incompressible Smoothed Particle Hydrodynamics (ISPH) software package ISPH3D which has been recognised the first open source ISPH software package in the world, which has wide application in the area of nuclear thermal hydraulics, offshore and marine energy industries, offshore oil and gas industries and coastal engineering consultancies involved in the design of coastal defences.


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Dr. Jony Castagna

I joined the Science and Technology Facilities Council (STFC) Hartree Centre in 2016 and since then I have enjoyed my work more and more. I am a computational scientist with a strong passion for high performance computing (HPC), usually oriented to the Simulation of Turbulent Flows using Computational Fluid Dynamics… But let me just say it: I love programming GPUs! CUDA is my favourite language, but I recently start to use OpenACC more due its portability to other platforms.  

Working with GPUs since 2010, how could I not end up in Deep Learning? This new fascinating world has recently captured me… and transformed me into an NVIDIA Ambassador here at STFC! So, while I enjoy running NVIDIA Deep Learning Institute courses ranging from CUDA to Deep Learning for Computer Vision, my main focus stays on applied HPC for scientific research, mainly using future computing systems like hybrid CPU-GPU architectures where integration between artificial intelligence (AI) and traditional HPC science is merged together.


Dr. Luke Mason 

I've worked in computational science for 15 years and started by developing control software for embedded and robotic systems before moving to high performance computing (HPC) 10 years ago. I have worked on a diverse range of HPC software over the years, from models of high velocity impact to porting weather and climate models to new computing architectures. I currently lead the High Performance Software Engineering Group at the Science and Technology Facilities Council (STFC) Hartree Centre. We specialise in code scalability and performance on HPC systems, as well as porting and optimisation for emerging technologies and novel architectures.

I enjoy working alongside both industry and academic scientists to produce accurate and efficient code across a range of disciplines and architectures. This Centre for Doctoral Training offers an excellent opportunity to develop new algorithms, optimised for the latest hardware and accelerate their up take into industry.  


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