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AI & Machine Learning (behaviour) PhD Projects, Programs & Scholarships

We have 23 AI & Machine Learning (behaviour) PhD Projects, Programs & Scholarships

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  Collective Behaviour of Autonomous Organisms: From Bio-Particles to Robotics
  Dr F Paillusson, Dr A Millard, Dr A Zvelindovsky
Application Deadline: 14 June 2020

Funding Type

PhD Type

Active Matter is an emerging interdisciplinary field in physics and applied mathematics which refers to systems comprising interacting agents which can drive their own motion (for instance birds, fish, insects, “smart” artificial micro-particles, or bio-mimicking robots).
  Edge Intelligence for Safety Assurance of Time Critical Cyber Physical Systems
  Dr S Kabir
Applications accepted all year round

Funding Type

PhD Type

Cyber-Physical-Systems (CPS) harbor the potential for vast economic and societal impact in domains such as mobility, home automation, energy, agriculture, and delivery of health.
  Identifying typologies of General Practitioners’ through analysis of prescribing decisions
  Prof T Marshall
Application Deadline: 31 October 2020

Funding Type

PhD Type

It has been demonstrated that used Facebook users’ “likes” can be used to reliably identify their personality characteristics and preferences.[1] This project proposes to use general practitioners’ (GPs’) prescribing decisions to characterise their treatment preferences and provide insights into variation in clinical behaviour between clinicians.
  Security-Aware Safety Monitoring of IoT-enabled Smart Environment
  Research Group: Applied Computing
  Dr S Kabir
Applications accepted all year round

Funding Type

PhD Type

Internet of Things (IoT) is a promising networking paradigm that integrates physical processes with the cyber world to provide useful services.
  Autonomous learning with Artificial Intelligence - (studied entirely by Distance Learning)
  Dr A Starkey, Dr M Giannaccini
Applications accepted all year round

Funding Type

PhD Type

Autonomous learning has become one of the latest buzzwords in today’s artificial intelligence and robotics. All the same, to realise the behaviour of even the simplest creature like a unicellular organism in an artificial agent remains extremely challenging with current artificial intelligence techniques.
  Bioinspired Robot Swarm for Monitoring Tasks
  Dr M Giannaccini, Dr A Starkey
Applications accepted all year round

Funding Type

PhD Type

Applications are invited for one doctoral position in robotics at the School of Engineering, University of Aberdeen. The successful candidate will undertake research focusing on bioinspired swarm robotics for monitoring applications.
  Digital twin in micromachining through multiscale simulation
  Dr H Tan, Dr Amir Siddiq
Applications accepted all year round

Funding Type

PhD Type

Digital twin technology in manufacturing is the simulation technology that integrates multi-disciplinary, multi-physical quantity, multiscale and multi probability by making full use of the physical model, monitoring update, operation history and other data.
  Energy system modelling of decentralised energy system transitions: integrating technical, economic and social dimensions
  Dr A Maheri
Application Deadline: 21 June 2020

Funding Type

PhD Type

Cities and municipalities are crucial areas of focus due to the large and growing populations living in them. But they are also complex dynamic systems, which present challenges to energy system modellers.
  Numerical simulation of water repellent soils
  Prof P Cleall, Prof A D Jefferson
Application Deadline: 31 July 2020

Funding Type

PhD Type

3 Years October 2020. This industrially supported project will focus on representing the processes related to, and assessing the impact of, hydrophobicity on soil behaviour via the development of novel models and their solution utilising established numerical techniques.
  Model-based security testing for Automotive Systems
  Dr H Nguyen
Application Deadline: 30 June 2020

Funding Type

PhD Type

In the modern automotive world, it is vital to make vehicles cyber-secure by understanding the risks and then engineering security requirements based on these risks.
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