Job Openings





Deadline TBD.

*NEW* KTP Associate in deep learning for person and crowd activity monitoring

In this role you will be employed and supervised by Professor Fabio Cuzzolin from the School of Engineering, Computing and Mathematics at Oxford Brookes University, but will be based with Createc in Oxford, leading a KTP project part-funded by Innovate UK. You will work on a cutting-edge new project aimed at developing an testing novel person and action recognition techniques, based on a combination of multiple sensor streams, eventually developing into a system able to monitoring the behaviour of groups of individuals or crowds. You will have research experience in machine learning for computer vision; a good publication record in these areas, as well as coding experience, and the passion to push academic research into real applications.

Qualifications we require: An MSc in Computer Science or a related discipline.
The successful candidate must have a degree and relevant experience as detailed in the full job specification on our website. Essential skills we require as a minimum: a good level of research, interpersonal and communication skills as detailed in the full job specification on our website.

Createc was founded by academics with who wanted to see their research changing the world for the better. We are a self-funded industrial R&D group, with a global track record of successful applications of Computer Vision and robotics to solve challenging problems. Find out more at https://www.createc.co.uk.

You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and an informal feedback on your application.

Salary: £30,000 to £35,000, plus personal development budget of £2k per annum.
Deadline for application: TBD

You can download the job ad here.


Deadline October 21st, 2018.

*NEW* Research Fellow in AI for Autonomous Driving

The School of Engineering, Computing and Mathematics at Oxford Brookes University is seeking a Research Fellow in Artificial Intelligence for Autonomous Driving, to be appointed as soon as possible, for a duration of 2 years. The Fellow will be appointed at Grade 8, with a starting salary of 31,302 per annum.

The successful candidate will lead the School's effort towards the development of human-aware AI for autonomous driving.

The project concerns the design and development of novel ways for robots and autonomous machines to interact with humans in a variety of emerging scenarios, with a focus on autonomous driving. We believe novel, disruptive applications of AI require neuroscience-inspired forms of communication between humans and machines much beyond the current level of sophistication. Smart cars need to understand that children and construction workers have different reasoning processes that lead to very different observable behaviour, in order to blend in with the road as a human-centred environment. Morally and socially appropriate behaviour is key to build trust and lead to acceptance from the public.

The Fellow will work to design and implement a prototype but complete pipeline in a simulated scenario, including: (i) the design of theory of mind simulations allowing smart cars to understand the reasoning and intention of other drivers and pedestrians; (ii) the making of decisions based on the results of these simulations; (iii) the actual control and path planning required to pursue the best course of action, with demonstration in a simulated environment.

They will also coordinate the effort of the three groups in the area by supervising MSc and final year students working on the subject, and liaising with our partners in Oxford, Cambridge and elsewhere.

As the project concerns artificial intelligence, mobile robotics and engineering aspects, the Fellow will work jointly with the Visual Artificial Intelligence, Cognitive Robotics and Autonomous Driving research groups, led by Prof Fabio Cuzzolin, Dr Matthias Rolf and Dr Andrew Bradley.
The Visual Artificial Intelligence Laboratory (http://cms.brookes.ac.uk/staff/FabioCuzzolin/) is a thriving unit projected to comprise 20 members in 2019, which has established itself as one of the top research groups in the world in deep learning for action detection, conducting work at the current boundaries of human action recognition.
The Engineering section has a strong reputation in motorsports and engagement with F1 teams, as demonstrated by Oxford Brookes Racing having been crowned Class 1 Runner Up in the 2018 Formula Student competition. The three groups can provide equipment including various multiple-GPU workstations, a significant number of humanoid robots (NAO, Baxter, Robothespian) as well as autonomous driving equipment and software.
A new dataset in Road Event and Activity Detection (READ), the first of its kind, is in the process of being released (https://arxiv.org/abs/1807.11332).

You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and an informal feedback on your application

Salary: £31,302 to £34,188 rising annually.
Deadline for application: October 21st, 2018

To apply, please follow the link below:

https://www.jobs.ac.uk/job/BMV175/research-fellow-in-ai-for-autonomous-driving


Deadline October 21st, 2018.

*NEW* Three Postdoctoral Researchers in deep learning for activity recognition in surgical robotics

The School of Engineering, Computing and Mathematics of Oxford Brookes University is seeking two Postdoctoral Researchers in deep learning for activity recognition and scene understanding in surgical robotics. The posts are offered fixed term, full time duration, until December 2020 included. The successful candidates will join the School's Artificial Intelligence and Vision lab to support the activities of the Horizon 2020 SARAS project (Smart Autonomous Robotic Assistant Surgeon):

http://cms.brookes.ac.uk/staff/FabioCuzzolin/projects.html#saras

The goal of the project is to design two robotics arms powered by an advanced cognitive AI capable of replacing human assistant surgeons in complex laparoscopic procedures. This involves an exciting combination of cognitive and sensorial tasks, namely: (1) recognising surgeon actions and events in real time; (2) placing what happens in the context of the overall surgical procedure; (3) making predictions about future surgeon action and anomalies; (4) understanding the surgical cavity, by detecting, labelling and segmenting scene elements; (5) tracking deformable surfaces and organs in real time.

Candidates should have a PhD or other Postgraduate qualification or be studying for PhD in a relevant subject, and possess significant experience in machine learning (especially deep learning), computer vision, and ideally both.

The successful candidates will join a vibrant and ambitious School that is welcoming, supportive and friendly. The School blends excellence in teaching and knowledge transfer with world-leading research in areas that span Artificial Intelligence, Computer Vision, Cognitive Robotics, Augmented Reality, Wireless Communications, e-Health and Human Machine Interfaces.
The AI and Vision lab, led by Professor Fabio Cuzzolin, enjoys a leadership position in the field of action detection and recognition, with the only online deep learning-based action detection platform capable of working in better than real time with top accuracy. The group has also strong interests in (statistical) machine learning, robust statistics and uncertainty theory, e-health, and applications to surgical and mobile robotics, working at the interface of AI and neuroscience. The team has strong links with top research groups in the UK, US and EU, and collaborates with a number of multinational and start-up companies.

International applicants from outside the EU will need to demonstrate their eligibility to work in the UK.

Salary: £31,302 to £34,188.
Deadline for application: October 21st, 2018

To apply, please follow the link below:

https://www.jobs.ac.uk/job/BMW712/postdoctoral-research-assistant

For informal feedback please contact Prof Fabio Cuzzolin: fabio.cuzzolin@brookes.ac.uk


Deadline May 20th, 2018

Research assistant position (12 months) in deep learning for activity recognition in surgical robotics

The School of Engineering, Computing and Mathematics of Oxford Brookes University is seeking a Research Assistant in Deep Learning for Vision in Surgical Robotics. This post is offered for a 12 month, fixed term, full time duration.
The successful candidates will join the School's Artificial Intelligence and Vision lab to support the activities of the Horizon 2020 SARAS project (Smart Autonomous Robotic Assistant Surgeon):

http://cms.brookes.ac.uk/staff/FabioCuzzolin/projects.html#saras

The goal of the project is to design two robotics arms powered by an advanced cognitive AI capable of replacing human assistant surgeons in complex laparoscopic procedures. This involves an exciting combination of cognitive and sensorial tasks, namely: (1) recognising surgeon actions and events in real time; (2) placing what happens in the context of the overall surgical procedure; (3) making predictions about future surgeon action and anomalies; (4) understanding the surgical cavity, by detecting, labelling and segmenting scene elements; (5) tracking deformable surfaces and organs in real time.

Candidates should have a PhD or other Postgraduate qualification or be studying for PhD in a relevant subject, and possess significant experience in machine learning (especially deep learning), computer vision, and ideally both.

The successful candidates will join a vibrant and ambitious School that is welcoming, supportive and friendly. The School blends excellence in teaching and knowledge transfer with world-leading research in areas that span Artificial Intelligence, Computer Vision, Cognitive Robotics, Augmented Reality, Wireless Communications, e-Health and Human Machine Interfaces.
The AI and Vision lab, led by Professor Fabio Cuzzolin, enjoys a leadership position in the field of action detection and recognition, with the only online deep learning-based action detection platform capable of working in better than real time with top accuracy. The group has also strong interests in (statistical) machine learning, robust statistics and uncertainty theory, e-health, and applications to surgical and mobile robotics, working at the interface of AI and neuroscience. The team has strong links with top research groups in the UK, US and EU, and collaborates with a number of multinational and start-up companies.

You will:

International applicants from outside the EU will need to demonstrate their eligibility to work in the UK.

Salary: £23,557 rising annually to £25,728.
Deadline for application: May 20 2018

To apply, please follow the link below:

http://www.jobs.ac.uk/job/BJJ902/research-assistant-in-deep-learning-for-vision-in-surgical-robots/

For informal feedback please contact Prof Fabio Cuzzolin: fabio.cuzzolin@brookes.ac.uk


Deadline: December 1st, 2016

Postdoctoral Researcher in Statistical Machine Learning

Salary: around £28K. This is a one-year post, further extension subject to funding.

The Department of Computing and Communication Technologies is seeking to appoint a Postdoctoral Research Assistant in Statistical Machine Learning in the Artificial Intelligence Laboratory, in order to kick-start a new research project on novel robust foundations for machine learning.

Machine learning algorithms typically focus on fitting a model to the available training data ('overfitting'), which may lead, for instance, an autonomous driving system to perform well on validation tests but fail catastrophically when tested in the real world (as it has unfortunately been demonstrated of late). Common practice in the field contemplates ‘generalisation’ criteria which are based on a rather naïve correlation between smoothness and generality.
With the deployment of machine learning algorithms in critical AI systems, however, it is crucial to ensure that these algorithms behave predictably 'in the wild'. This raises issues with Vapnik's traditional Statistical Learning Theory as a viable foundation for robust machine learning. The remarkable empirical performance achieved so far by deep learning-based approaches, on the other hand, is yet to be matched by a satisfactory understanding of the theoretical basis for their robust behaviour.

The successful candidate will take the leadership of a new research programme designed at laying the groundwork for a new, robust paradigm for the foundations of machine learning. A number of research avenues can be envisaged. Worst-case, cautious predictions may be generated by solving appropriate mini-max optimisation problems. Robust analyses based on convex sets of models (e.g. convex sets of linear boundaries) appear promising. Finally, robust Bayesian analysis and random set theory may provide the means for a generalisation of the concept of “Probably Approximately Correct” in statistical learning theory.

Candidates should have a PhD or other Postgraduate qualification or be studying for PhD in a relevant subject and have significant experience in machine learning, statistics or both.
The successful candidate will join a vibrant and ambitious department that is welcoming, supportive and friendly. The department blends excellence in teaching and knowledge transfer with world-leading research in areas that span Artificial Intelligence, Computer Vision, Cognitive Robotics, Augmented Reality, Wireless Communications and Human Machine Interfaces.

Closing date: December 1st, 2016


July 12 2015

UAI 2015 Tutorial PDF - Belief functions for the working scientist - VIDEO NOW ONLINE!

The theory of belief functions, sometimes referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general framework for modelling epistemic uncertainty. The methodology is now well established as a general framework for reasoning with uncertainty, with well-understood connections to related frameworks such as probability, possibility, random set and imprecise probability theories. Importantly, in recent years the number of papers published on the theory and application of belief functions has been booming (reaching over 800 in 2014 alone), displaying strong growth in particular in the East Asian community and among practitioners working on multi-criteria decision making, earth sciences, and sensor fusion.

Belief functions are a natural tool to cope with heavy uncertainty, lack of evidence and missing data, and extremely rare events. An early debate on the rationale of belief functions gave a strong contribution to the growth and success of the UAI community and series of conference in the Eighties and Nineties, thanks to the contribution of scientists of the caliber of Glenn Shafer, Judea Pearl, Philippe Smets and Prakash Shenoy, among others. Ever since the UAI and BELIEF community have somewhat diverged, and the proposers' effort has been recently directed towards going back to a closer relationships and exchange of ideas between the two communities.

This was one of the aims of the recent BELIEF 2014 International Conference of which the proposers were General Chair and member of the Steering Committee, respectively. A number of books are being published on the subject as we speak, and the impact of the belief function approach to uncertainty is growing. The proposed tutorial aims at bridging the gap between researchers in the field and the wider AI and Uncertainty Theory community, with the longer term goal of a more fruitful collaboration and dissemination of ideas.


Deadline: May 1 2015

Computer Vision Development Engineer (KTP Associate)

Salary: Up to 30k pounds pa depending on qualifications and experience, plus generous training budget. This is a fixed-term post for 24 months.

This challenging post for a computer vision specialist needs creative thinking to generate original inspection solutions that will shape the future technology of a world-leading company.

Meta Vision Systems, located close to Oxford, UK, has designed a project in collaboration with Oxford Brookes University and the partners are seeking a highly motivated, Master's qualified individual to lead this development over two years.

Based at the company but working under supervision from the university, you will have access to people and facilities in both organisations, including the Oxford Brookes AI and Vision group. You will also work with the company's clients, designing customised algorithms to meet their requirements.

Meta Vision Systems works internationally with major companies in sectors as diverse as automotive, aerospace, robotics and general fabrication, supplying its innovative laser vision systems for material joining. On successful completion of the project the company expects to offer a permanent position.

You will have carried out Master's level research in computer vision or machine learning, and you will be proficient in coding. You will benefit from a generous training budget and as part of the long-established Knowledge Transfer Partnerships programme you will have access to hundreds of others engaged in KTP projects across the UK.

For information see www.meta-mvs.com, http://cct.brookes.ac.uk and http://ktp.innovateuk.org.
To apply, see www.brookes.ac.uk/vacancies Ref: 436/19288/BC

Closing date: 1 May 2015 Interview date: 14 May 2015


Deadline: October 31 2014

PhD Studentship - "Real-time Action Recognition for Human-Robot Interaction"

The Faculty of Technology, Design and the Environment at Oxford Brookes University is pleased to offer a three year full-time PhD Studentship to a new student commencing in January 2015. The successful applicant will receive an annual bursary of £10,000 for three years (with no inflation increase) and the fees will be paid by the University.

The successful candidate will work within the Department of Computing and Communication Technologies as part of both the Artificial Intelligence and Vision group and the Cognitive Robotics group, under the supervision of Dr Fabio Cuzzolin and Dr Nigel Crook.

Topic of research: Real-time Action Recognition for Human-Robot Interaction

Action recognition is a fast-growing area of research in computer vision. The problem consists in, given a video captured by one or more cameras, detecting and recognising the category of the action performed by the person(s) who appear in the video. The problem is very challenging, for a number of reasons: labelling videos is an ambiguous task, as the same sequence can be assigned different verbal descriptions by different human observers; different motions can carry the same meaning (inherent variability); nuisance factors such as viewpoint, illumination variations, occlusion (as parts of the moving person can be hidden behind objects or other people) further complicate recognition. In addition, traditional action recognition benchmarks are based on a 'batch' philosophy: it is assumed that a single action is present within each video, and videos are processed as a whole, typically via algorithms which require entire days to be completed. This can be ok for tasks such as video browsing and retrieval over the internet (although speed is a huge issue there), but is completely unacceptable for a number of real world applications which require a prompt, real-time interpretation of what is going on. Examples are: human-robot and human-machine interaction (using gestures to send commands to a computer or a robot), surveillance (detecting potentially dangerous actions or events in live feeds), car driver's monitoring (monitoring the level of attention, or responding to gestural commands), gaming (interpreting the body language of a video game player), intelligent vehicles (understanding the behaviour of pedestrians and other vehicles in the vicinity of a car). Consequently, a new paradigm of online, real-time action recognition is rapidly emerging, and is likely to shape the field in coming years. The AI and Vision group is already building on its multi-year experience in batch action recognition to expand towards online recognition, based on two distinct approaches: one based on the application of novel 'deep learning' neural networks to automatically segmented video regions, the other resting on continually updating an approximation of the space of feature measurements extracted from images, via a set of balls of radius which depends on how difficult classification is within that region of the space.

For further information about the Artificial Intelligence and Vision group, consult http://cct.brookes.ac.uk/research/isec/artificial-intelligence/ and Fabio Cuzzolin's web page: http://cms.brookes.ac.uk/staff/FabioCuzzolin/
For information on the Cognitive Robotics group, consult http://cct.brookes.ac.uk/research/isec/cognitive-robotics/index.html

The selection criteria will focus on academic excellence, suitability of research experience and skills, subject knowledge and references.


Deadline: February 28 2014

PhD Studentship - "Uncertainty in Computer Vision"

The Department of Computing and Communications Technologies at Oxford Brookes University is pleased to offer a three year full-time PhD Studentship to a new student commencing in June 2014. The successful applicant will receive an annual bursary of 10,000 pounds for three years (with no inflation increase) and the fees will be paid by the Universty.
The successful candidate will work within the Artificial Intelligence and Vision group of the Department of Computing and Communication Technologies, under the supervision of Dr Fabio Cuzzolin.

Topic of research: Uncertainty in Computer Vision

Decision making and estimation are central in most applied sciences, as the need often arises to make inferences about the state of the external world, based on information which is at best limited, if not downright misleading. Uncertainty can be dealt with in a number of ways. Generative probabilistic graphical models, which describe how the data are generated via classical distribution functions, are most used for complex, multi-person activity recognition. Discriminative models which do not attempt to model data generation, but focus on learning how to discriminate between data belonging to different categories or classes, are dominant in action and gesture recognition, in which we aim at recognising human actions based on a limited training set of examples, captured via conventional or range cameras. Imprecise-probabilistic models which assume the data is probabilistic but insufficient to estimate a precise probability distribution, have been successfully employed in example-based human pose estimation. Depending on the problem we need to tackle, we might need to consider metric learning techniques for generative models, latent SVM part-based discriminative approaches, or a meaningful integration of the two.

The successful candidate will work on both the theoretical development and the application of these techniques to scenarios such as the interaction with a humanoid robot able to recognise and mimic natural human gesturing, the retrieval of videos from internet repositories such as YouTube, the monitoring of the health of people affected by brain conditions in their own homes, via range sensors such as Kinect.

For further information about the Artificial Intelligence research group, consult

http://cct.brookes.ac.uk/research/isec/artificial-intelligence/index.html

or Dr Fabio Cuzzolin's web page:

http://cms.brookes.ac.uk/staff/FabioCuzzolin/

The selection criteria will focus on academic excellence, suitability of the research environment for your project and references.

If you would like to apply you should submit an application for a place on the PhD programme at Oxford Brookes University through UKPASS (http://www.ukpass.ac.uk). As part of the application you must also submit a full research proposal (instructions on how to prepare a research proposal can be found on the Brookes website: http://cct.brookes.ac.uk/research/proposals.html) together with a supporting statement of no more than 500 words summarising:

Your reasons for undertaking this project; Preparation undertaken and previous research experience; The ways in which this bursary will make a difference to your research.

Please apply through UKPass and submit all of the following supporting documents separately to Helen Tanner - htanner@brookes.ac.uk - by 12noon on 28 February 2014.

Full research proposal
Copy of Passport (if applicable)
IELTS Certificate or equivalent (if applicable)
Two academic references
Degree Certificate(s)
Transcript(s)

Please be advised that the selection process may involve an interview, and the successful candidate would be expected to commence in the research degree programme in June 2014.


January 2014

Article on Research Media "Innovation International" on Fabio's project on tensorial models for gait identification

Fabio has published an article on Research Media's "International Innovation" magazine on his EPSRC project "Tensorial modeling of dynamical systems for gait and activity recognition".

International Innovation published by Research Media is the leading global dissemination resource for the wider scientific, technology and research communities, dedicated to disseminating the latest science, research and technological innovations on a global level. More information and a complimentary subscription offer to the publication can be found at: www.researchmedia.eu.

Full article in high resolution PDF format


September 26-29, 2014

BELIEF 2014 - The 3rd International Conference on Belief Functions

The theory of belief functions, also referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories.

In 2012 alone, more than 300 papers on belief functions and their applications have been published worldwide. The ambition of the series of International Conferences on Belief Functions - BELIEF - is to bring together the large and expanding community of mathematicians, statisticians, computer scientists, engineers, economists and practitioners which work on the theoretical foundations of belief calculus or its application to all fields of applied science.

This 3rd edition, in particular, aims at more closely involving in the community the many specialists of other fields which use belief functions in their daily work, improving the overall visibility of the field by pushing towards a more coalesced and tightly connected community, and reaching out towards the sibling fields of uncertainty theory, Bayesian reasoning, imprecise probability and fuzzy theory.

The conference will provide opportunities to exchange ideas and present new results on both the theory and applications of belief functions and related areas such as random sets, imprecise probability and possibility theory.
Original contributions are solicited on theoretical aspects, including:
  • decision making
  • combination rules
  • conditioning
  • continuous belief functions
  • independence and graphical models
  • statistical inference
  • geometry and distance metrics
  • mathematical foundations
  • computational frameworks
as well as on applications in various areas including, but not limited to:
  • data and information fusion
  • pattern recognition
  • machine learning and clustering
  • tracking and data association
  • data mining
  • signal and image processing
  • computer vision
  • medical diagnosis
  • business decision
  • risk analysis
  • engineering and environment
  • climatic change
Papers will be presented orally during the conference in a single track session, or in poster sessions.

Full paper submission deadline: April 30th, 2014 (see IMPORTANT DATES).

Authors of selected papers from the BELIEF 2014 conference will be invited to submit an extended version of their papers for possible inclusion in a special issue of the International Journal of Approximate Reasoning.