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Postdoctoral Researcher In Deep Learning For Activity Recognition

Oxford Brookes University - Technology, Design And Environment

Location: Oxford
Salary: £31,302 to £34,188
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 25th January 2019
Closes: 28th February 2019
Job Ref: 062662

£31,302, rising annually to £34,188

Full Time, Fixed Term - Researcher

The School of Engineering, Computing and Mathematics of Oxford Brookes University is seeking a Postdoctoral Research Assistant in deep learning for activity recognition and scene understanding in surgical robotics.

The post is offered on a full time, fixed term contract until 31 December 2020. The successful candidate will join the School's Visual Artificial Intelligence Laboratory to support the activities of the Horizon 2020 SARAS project (Smart Autonomous Robotic Assistant Surgeon).

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.

You 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.

If successful you 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 accuracies. 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.

As Postdoctoral Researcher you will:

  • Take primary responsibility for the research activities assigned to Oxford Brookes University as part of the SARAS project;
  • Assume day-to-day responsibility for SARAS, including financial monitoring and managing of equipment, assisting with the leadership of WorkPackage 6;
  • Work closely with Prof Cuzzolin and his team on this and other projects and studies relating to machine learning and computer vision;
  • Assist with the supervision of PhD, visiting and MSc students in the lab;
  • Be willing to undertake a small amount of teaching.

You should have:

  • Postgraduate or Postdoctoral qualification or studying for a PhD;
  • Research experience in machine learning for computer vision;
  • Significant publication record in top vision and machine learning venues;
  • Experience of coding machine learning algorithms.

As one of the largest employers in Oxford we pride ourselves in the great experience we offer our staff. You'll be joining a friendly, professional environment where every member of staff is recognised as important to the success of Oxford Brookes University. To find out more about the benefits of working for Oxford Brookes please visit:

The University has adopted equality, diversity and inclusion as core values. We welcome applications from suitably qualified candidates whatever their background, and especially from BAME candidates who are under-represented in our workforce.

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