PhD Scholarship - Wearable Machine Learning for Human Activity Recognition, University of Sussex (UK)
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Daniel Roggen
May 24, 2018, 9:14:25 AM5/24/18
to Machine Learning News
A 4-year PhD position is available in the Wearable Technologies Lab (Dr. Daniel Roggen) at the University of Sussex (UK). The position is in the field of machine learning, artificial intelligence, wearable sensing and activity recognition. It is part of an industrial collaboration with a UK multinational which will offer the opportunity for a 3-month placement within that company.
Closing date for applications: 15 June, 2018
== Scholarship ==
- Only suitable for UK residents for more than 3 years (e.g. undergrad done in the UK)
- 4-year scholarship
- £16,500 tax free stipend per year (£2K above the usual UK stipend)
- Fees are waived
== Objectives ==
The aim of this project is to develop novel wearable systems comprising advanced machine learning techniques (e.g. deep learning) and novel sensor modalities which are capable of recognising a wide variety of activities of daily living related to personal care (e.g. brushing teeth, washing hands, combing hair), and to quantify the manner in which these ADLs are performed over time. This will be evaluated in user studies in scenarios related to improving quality of life.
== Details ==
Within this project, you will develop advanced machine learning and AI techniques suitable for use with wearable sensor data, and explore novel wearable sensors (beyond motion sensors) which are relevant to the recognition of activities of daily living. Such sensors may include, for example, those allowing to measure the relative orientation of body parts, to sense distance to objects, sense indoor location, or capture indirect effects of human movement.
Machine learning and AI techniques should be tailored to wearable sensor data and research may be directed towards achieving high-performance (e.g. with deep learning) or addressing the challenge of efficient algorithm design for execution on embedded systems. This project will contribute to extending our existing wearable sensing platform or design a new one.
You will characterise the ability of the resulting system at recognising a wide range of ADLs through user studies at the University and with our industrial partner.
As part of this project, you will communicate frequently with our industrial partenr - a UK multinational - to share research findings. You will also have the opportunity to do a placement
within that company.
== Qualifications ==
The ideal candidates will have a master's degree in computer science, computer engineering, physics, electrical engineering, or equivalent, with prior experience in machine learning and/or embedded and wearable systems.
The ideal candidates will have a passion to contribute to the development of novel wearables which can improve our quality of life. They will have outstanding technical skills and a strong interest in research at the crossroads of signal processing, machine learning, embedded systems, sensor technologies and their applications.
Applicants should be committed to pursue leading research and publish results in top venues. Additionally, we expect mastery of written and spoken English, self-motivation, an inquiring mind, be able to work independently and in an interdisciplinary environment.
The selected candidate will join an interdisciplinary research centre with state of the art computing and electronics facilities and a wide range of technologies at hand: augmented reality glasses, smartwatches, novel sensor technologies and wearable systems, etc.
Some of the research areas you may come in contact during your PhD include applied machine learning, deep learning, signal processing, big data, sensor technologies, embedded system development, human-computer interaction, etc.
== How to apply ==
Contact Dr. Daniel Roggen (daniel...@ieee.org) with your CV to discuss your application.
Applications are handled online via the University Doctoral School:
http://www.sussex.ac.uk/study/phd/apply.
More informations about the Wearable Technologies Lab and ongoing research:
http://www.sussex.ac.uk/strc/research/wearable
http://scholar.google.co.uk/citations?user=JGjtLtYAAAAJ
Closing date for applications: 15 June, 2018
== Scholarship ==
- Only suitable for UK residents for more than 3 years (e.g. undergrad done in the UK)
- 4-year scholarship
- £16,500 tax free stipend per year (£2K above the usual UK stipend)
- Fees are waived
== Objectives ==
The aim of this project is to develop novel wearable systems comprising advanced machine learning techniques (e.g. deep learning) and novel sensor modalities which are capable of recognising a wide variety of activities of daily living related to personal care (e.g. brushing teeth, washing hands, combing hair), and to quantify the manner in which these ADLs are performed over time. This will be evaluated in user studies in scenarios related to improving quality of life.
== Details ==
Within this project, you will develop advanced machine learning and AI techniques suitable for use with wearable sensor data, and explore novel wearable sensors (beyond motion sensors) which are relevant to the recognition of activities of daily living. Such sensors may include, for example, those allowing to measure the relative orientation of body parts, to sense distance to objects, sense indoor location, or capture indirect effects of human movement.
Machine learning and AI techniques should be tailored to wearable sensor data and research may be directed towards achieving high-performance (e.g. with deep learning) or addressing the challenge of efficient algorithm design for execution on embedded systems. This project will contribute to extending our existing wearable sensing platform or design a new one.
You will characterise the ability of the resulting system at recognising a wide range of ADLs through user studies at the University and with our industrial partner.
As part of this project, you will communicate frequently with our industrial partenr - a UK multinational - to share research findings. You will also have the opportunity to do a placement
within that company.
== Qualifications ==
The ideal candidates will have a master's degree in computer science, computer engineering, physics, electrical engineering, or equivalent, with prior experience in machine learning and/or embedded and wearable systems.
The ideal candidates will have a passion to contribute to the development of novel wearables which can improve our quality of life. They will have outstanding technical skills and a strong interest in research at the crossroads of signal processing, machine learning, embedded systems, sensor technologies and their applications.
Applicants should be committed to pursue leading research and publish results in top venues. Additionally, we expect mastery of written and spoken English, self-motivation, an inquiring mind, be able to work independently and in an interdisciplinary environment.
The selected candidate will join an interdisciplinary research centre with state of the art computing and electronics facilities and a wide range of technologies at hand: augmented reality glasses, smartwatches, novel sensor technologies and wearable systems, etc.
Some of the research areas you may come in contact during your PhD include applied machine learning, deep learning, signal processing, big data, sensor technologies, embedded system development, human-computer interaction, etc.
== How to apply ==
Contact Dr. Daniel Roggen (daniel...@ieee.org) with your CV to discuss your application.
Applications are handled online via the University Doctoral School:
http://www.sussex.ac.uk/study/phd/apply.
More informations about the Wearable Technologies Lab and ongoing research:
http://www.sussex.ac.uk/strc/research/wearable
http://scholar.google.co.uk/citations?user=JGjtLtYAAAAJ
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