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Prof Nick Jones
Applications accepted all year round
Competition Funded PhD Project (European/UK Students Only)
About the Project
PhD Project: Imperial College Mathematics
Student Background: Theoretical Physics, Mathematics/Statistics, Engineering.
What are the common patterns in the world? And what classes of mechanisms generate them? This project considers a distinctive, empirically driven, approach to these questions.
We have recently constructed a library on an unprecedented scale, of over 1000 scientific tools to study signals (time-series). These were taken from across science (from physics, statistics, engineering, computer science, econometrics etc) and have been automated so that a single signal is simultaneously studied in thousands of ways. Early exploration with this tool has shown it is useful for identifying Parkinsonian speech, identifying pathological heart rhythms and even distinguishing seismograms for small earthquakes from man-made explosions. It also helps automatically organise parts of the methodological literature and can be used to identify generic patterns in natural signals and to automatically suggest classes of suitable models. Since approaches like this haven't previously been developed there's real space for students to make a substantial contribution.
This project is to be undertaken in close collaboration with Norman MacLeod at the Natural History Museum. Prof MacLeod is a leading morphometrician and we will be seeking to apply morphometric analyses to audio signals. The student will, further, have the opportunity to participate in summer projects related to the Museum's commercial activities.
This project requires a mix of physical science intuition, computer science algorithms and statistical inference. A background from across the physical and mathematical sciences would be suitable.
You can learn about the research of the systems and signals group on our site:
http://www2.imperial.ac.uk/~nsjones/
and from our blog:
http://systems-signals.blogspot.co.uk/2013/04/a-compound-methodological-eye-on.html
Further enquiries - contact with a CV detailing academic performance (i.e. including as detailed as possible information about grades/marks or equivalent):
Nick Jones (Imperial Mathematics) http://www2.imperial.ac.uk/~nsjones/
Student Background: Theoretical Physics, Mathematics/Statistics, Engineering.
What are the common patterns in the world? And what classes of mechanisms generate them? This project considers a distinctive, empirically driven, approach to these questions.
We have recently constructed a library on an unprecedented scale, of over 1000 scientific tools to study signals (time-series). These were taken from across science (from physics, statistics, engineering, computer science, econometrics etc) and have been automated so that a single signal is simultaneously studied in thousands of ways. Early exploration with this tool has shown it is useful for identifying Parkinsonian speech, identifying pathological heart rhythms and even distinguishing seismograms for small earthquakes from man-made explosions. It also helps automatically organise parts of the methodological literature and can be used to identify generic patterns in natural signals and to automatically suggest classes of suitable models. Since approaches like this haven't previously been developed there's real space for students to make a substantial contribution.
This project is to be undertaken in close collaboration with Norman MacLeod at the Natural History Museum. Prof MacLeod is a leading morphometrician and we will be seeking to apply morphometric analyses to audio signals. The student will, further, have the opportunity to participate in summer projects related to the Museum's commercial activities.
This project requires a mix of physical science intuition, computer science algorithms and statistical inference. A background from across the physical and mathematical sciences would be suitable.
You can learn about the research of the systems and signals group on our site:
http://www2.imperial.ac.uk/~nsjones/
and from our blog:
http://systems-signals.blogspot.co.uk/2013/04/a-compound-methodological-eye-on.html
Further enquiries - contact with a CV detailing academic performance (i.e. including as detailed as possible information about grades/marks or equivalent):
Nick Jones (Imperial Mathematics) http://www2.imperial.ac.uk/~nsjones/
References
Highly comparative time-series analysis: the empirical structure of time series and their methods
http://rsif.royalsocietypublishing.org/content/10/83/20130048.full.pdf+html
http://rsif.royalsocietypublishing.org/content/10/83/20130048.full.pdf+html
Project supervisors
Career overview
Prof. Nick Jones is a Professor of Mathematical Sciences in the Department of Mathematics at Imperial College London, within the Faculty of Natural Sciences. His research interests encompass a variety of fields, including Biochemistry and Cell Biology, Clinical Sciences, Ecology, Genetics, Evolutionary Biology, Neurosciences, Biological Physics, Applied Statistics, Knowledge Representation and Machine Learning, Signal Processing, and Public Health and Health Services. Prof. Jones has contributed significantly to the understanding of complex biological systems through mathematical modelling and statistical analysis. His work often involves interdisciplinary collaboration, leveraging mathematical techniques to address challenges in biological and health-related contexts. Prof. Jones is also associated with several research groups and initiatives, including the I-X Centre for AI in Science and the EPSRC Centre for Maths of Precision Healthcare, among others.
Research interests
Professor Nick Jones''s research encompasses a wide range of fields including biochemistry and cell biology, clinical sciences, ecology, genetics, evolutionary biology, neurosciences, biological physics, applied statistics, knowledge representation and machine learning, signal processing, public health and health services, and mathematical sciences. His work focuses on the dynamics of mitochondrial DNA, cellular behaviour, and the application of mathematical models to biological systems. He is particularly interested in the stochastic modelling of cellular processes, the influence of network structures on biological functions, and the integration of machine learning techniques in biological research.
