Applications for suitably qualified PhD students are accepted all year round.  A list of some recently advertised projects are listed below.  Please see more information about how to apply for a PhD.

We have world-leading expertise in Vision, Image and Signal Processing, Ocean Systems, Microengineering, Microwaves and Electrical Power and Drives. We also participate in several research pooling initiatives.

Our team of world-leading scientists and engineers has expertise across a broad field of electrical engineering and access to state-of-art facilities, which can assist our industrial partners to resolve existing technical problems and secure leading position in future markets.

You may contact the named supervisor about a particular project opportunity. For more general enquiries, please contact Dr Changhai Wang or Karen Paterson in our postgraduate research office.

SP Energy is looking for motivated and enthusiastic undergraduate students

SP Energy is looking for motivated and enthusiastic undergraduate students who are interested in a career in the energy industry.

Successful Scholars will benefit from:

-       An annual Scholarship payment of £3,000.
-       A paid work placement during the summer break delivering engineering projects.
-       Opportunities to build a relationship with SP Energy.
-       Company Mentor.
-       Accelerated graduate recruitment process for high performing candidates.

Applications are open now.

You should complete an application form. To obtain a copy of this please contact Lynn Smith, Institute Administrator, in the first instance.  Completed forms shold be submitted by the deadline of 5pm, Tuesday 31st January 2017.

Dr David Flynn is the academic point of contact if students wish to discuss their application or to learn more about the opportunity. Dr Flynn can be contacted at: d.flynn@hw.ac.uk

Multiple Vehicles Maritime Autonomy for Oceanography

Monitoring oceanographic phenomena, be they physical, chemical or biological requires sample data, taken at the right place, time and frequency to detect, qualify and model them. Currently, this is typically done from ships, moored or drifting platforms with limited embedded intelligence and no ability to modify their trajectories to adapt their sampling strategy. Gliders and autonomous surface and underwater vehicles have recently been introduced and have the ability to perform 3 dimensional sampling whilst controlling their location precisely. However, they are currently using pre-planned missions and do not adapt to the sensors measurements. They also do not collaborate as a team to tackle a joint mission optimally. As these platforms mature and become a commodity, they have the potential to become a powerful and adaptable sensor network. What is required is the autonomy framework to manage the collaboration of the platforms together with the signal processing theory to understand the core issues of sparse sampling and reconstruction of the underlying phenomenon that network is observing, whilst taking into account the communications limitation of the underwater domain.

We propose to develop an autonomy architecture which tackles this generic problem (mobile sensor networks sampling theory) and apply it to one (or more) oceanographic problems. The target applications will involve the deployment of surface and underwater vehicles, equipped with relevant sensors (chemical, biological) whose collective target is to detect and monitor a specific scientific event by adapting the sampling strategy of the network to provide the best possible model of the observed event. We expect the architecture to be fully decentralized (each platforms takes its own decision based on information provided by its neighbours) and its adaptive behaviours to be driven by a mixture of offline modeling of the phenomenon and on-line adaptation to live sensor data. The system will be developed in collaboration with SeeByte ltd who have been involved in an SBRI project with NOC on Adaptive Autonomous Ocean Sampling Networks and are interested in sponsoring a Case studentship to continue this line of research. The validation would be performed in Heriot-Watt university on the Oceans Systems Lab assets in the initial stages but would eventually be tested on a real scientific issue using the MARS fleet.

Two scientific applications are envisaged to guide the development: open ocean deep convection and spring phytoplankton blooms.  (1) Open ocean deep convection is a sporadic process where wintertime atmospheric cooling can make surface waters sufficiently dense to mix deeply (to 1000m or more).  A field of convecting plumes is expected to be in a localized region of water---O(100km) across---surrounded by stratified water.  The convective region is dotted with narrow mixing plumes (O(100m) across) where.  A decentralized sampling system would enable near-real time mapping of the convective patch, where underwater vehicles could identify and characterize the boundary between convecting and stratified water, and higher resolution sampling within mixing plumes.  (2) Phytoplankton blooms occur in the springtime, and are characterized by “patchy” areas of high fluorescence or production, interspersed with areas of relatively weak production.  An adaptive sampling network could be applied to find, and then characterize, the spatial scales of phytoplankton production at high resolution.  In both cases (1) and (2), the underwater sampling can be enhanced by surface measurements including meteorological data (in the case of convection) and irradiance (in the case of production).

The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at Heriot-Watt University, and will share time between Heriot-Watt and SeeByte.

The student will work in a multi-disciplinary team of engineers and scientists. The student will get exposure to the commercial world through its links and time spent at SeeByte ltd. He/She will also get a strong understanding of the embedded software required to smarten the current generation of autonomous systems and will be able to adapt them to new problems in the future.

For informal enquiries please contact Prof Yvan Petillot, Head of Sensors, Signals & Systems at Heriot-Watt University.

EPS Funded PhD Scholarship on Robotics

The School of Engineering and Physical Sciences (EPS) at Heriot-Watt University is offering full fees and stipend for 3 years fully-funded PhD studentship to UK/EU applicants on physical human-robot interaction and assistive robotics with medical application. Heriot-Watt University is recognised as one of the leading UK research institutes in general engineering as per the Research Excellence Framework (REF) results of 2014 (ranked 1st). The student will be supervised by Dr Mustafa Suphi Erden of the Institute of Sensors, Signals, and Systems (ISSS) based at EPS at Heriot-Watt University.

This PhD project is an integral part of a wider research on developing robotic trainers and assistants for minimally invasive surgery (MIS) operations in the medical domain. The research includes capturing surgeon skills, developing physically interactive robotic trainers for haptic and manipulation skills, developing physically interactive robotic assistants for co-manipulation, identifying surgeon skill level through electromyography (EMG) registering from arm muscles and through near-infrared spectroscopy (NIRS) monitoring of cortical brain activity. The PhD student will work in close cooperation with other PhDs and post-doctoral researchers.

Specific to this position are,

1) Taking part in skill capturing for MIS through kinematics analysis,

2) Taking part in robotic measurement of surgeon hand-impedance and their analysis,

3) NIRS cortical brain monitoring for MIS skill level identification,

4) Developing robotic trainer for MIS with feedback from NIRS monitoring,

5) Developing robotic assistant for MIS with feedback from NIRS monitoring,

6) Taking part in integration of the robotic trainer and assistant with feedback from EMG registering.

The project requires knowledge and experience with robotic manipulators and machine learning techniques. Knowledge and experience with medical robotics and medical applications is desirable.

Physical Human-Robot Interaction and Assistive Robotics at ISSS

The physical human-robot interaction and assistive robotics research group aims to develop both robotic assistance and robotic training technologies for industrial and medical applications. Their research focuses on (1) understanding human behavior and human factors in manipulation tasks within the actual task environment, (2) design, control, and implementation of robotic assistants to help humans in these tasks by using knowledge of human behaviour and human factors, and (3) design, control, and implementation of robotic trainers to ameliorate and speed up training of novice subjects. One of the streams of their research is fine manipulation tasks requiring professional skills, such as manual welding in industry and minimally invasive surgery in medicine.

How to Apply

Applications should be made online. Applications will be accepted up to the point of recruiting a suitable candidate. Enquires about the research project should be made to Dr Mustafa Suphi Erden.

Besides the formal forms and papers for the online application, the following are required:

1) A motivation letter (at most two pages) stating your background in relation to the project and why you are interested in the position,

2) A transcript of bachelor and master program grades,

3) Any conference or journal publications and Master thesis or dissertation you have written.

Fully Funded PhD Studentship co-sponsored by Baker Hughes

The EPSRC Centre for Doctoral Training (CDT) in Embedded Intelligence (EI) at Heriot-Watt University is offering two fully-funded PhD studentship to UK/EU applicants. Heriot-Watt University is recognised as the leading UK research institute in general engineering as per the Research Excellence Framework (REF) results of 2014. The CDT in EI represents a £13M centre covering the integration of intelligence into products, processes and services so they work better and can increase productivity, efficiency and connectivity. The students will be supervised by Dr David Flynn and Dr Valentin Robu of the Smart Systems Group based at Heriot-Watt University.

The central theme of these studentships will be prognostics and health management. The PhD students will spend periods of time working as a team within Baker Hughes as well as Heriot-Watt University. The students will review literature in prognostics and health management (PHM), review test and design specifications of sub-systems, develop design of experiment tests for lifecycle analysis and establish the failure mode mechanism and effect analysis (FMMEA).

This studentship will adopt a data analytics approach to predicting the remaining useful life (RUL) of assets. This will involve the investigation of statistical methods within machine learning and artificial intelligence. This position is best suited to someone with a computer science, software engineering, mathematics or embedded systems background.

In the final 12 months of research the students will spend some of their time collaborating on the potential of Fusion Prognostics. Fusion prognostics seeks to utilise a hybrid version of data and modelling based methods in order to improve the accuracy of RUL predictions.

Baker Hughes is a top-tier oilfield service company with a century-long track record in delivering solutions that help oil and gas operators make the most of their reservoirs. A history of technology innovation is a cornerstone of their success. Local teams are supported by global centres of excellence where scientists push the boundaries of value-adding technology to find solutions for progressively more complex technical challenges. Baker Hughes have a distinguish track record of academic engagement reflected through its strategic Alliance with Heriot-Watt University.

Baker Hughes will provide technical support and continuous professional development resources to this project beyond the studentship funding, including: technical supervision, access to historical data and design of assets, lifecycle test facilities, in-situ monitoring of assets and technical training. The engineering expertise and test facilities of Baker Hughes are critical assets to the program of research that students are expected to fully utilise.

Smart Systems Group
A primary research theme of the Smart Systems Group (SSG) is PHM, the driver for this activity is to provide new visibility and understanding to the remaining useful life of critical assets. High value assets are deployed in harsh environments across a myriad of industrial sectors, ranging from aviation, space, subsea and energy. These environments are exposed to high temperature, pressure, radiation, shock values, as well as chemically corrosive conditions. Assets in such environments present significant challenges in terms of their design, operation and maintenance due to limitations in the visibility of the asset within the harsh environment. The ability to monitor these assets is impeded by the thermal limits of materials, aggressive ambient conditions influencing sensor drift and failure, power management, data analysis and communication issues. The research vision of this group is to design, manufacture, package and test novel smart microsystems that are reliable, robust, adaptable and impervious to harsh environments to support the intelligent management of high value assets.

Applicants should
• Have a first class honours or good upper second class degree in Materials Science, Computer Science, Mathematics or Embedded Systems or equivalent
• With respect to studentship (1) the candidate should have an understanding of statistical methods of analysis, data acquisition, data processing and modelling.
• Candidates should demonstrate a keen interest in FMMEA, prognostics, sensors and systems engineering.
• Working across research teams within academia and industry the candidates will require good organisational and communication skills.
• Experience or knowledge of design methodologies is desirable but not essential.
• Meet the minimum English Language requirements, details available on the website
• Satisfy the UK residency requirement – see EPSRC eligibility criteria

How to Apply
Applications can be made online at : https://www.hw.ac.uk/schools/engineering-physical-sciences/research/phd/isss.htm
Informal enquires about the research projects should be made to Dr David Flynn (d.flynn@hw.ac.uk).

Each studentship will run for 4 years and include:

A fee waiver equivalent to the home/EU rate.
An enhanced EPSRC tax-free stipend of up to £ £17, 057 p.a. for four years.
A personal training budget of £10,000 to support specific training needs.
Periods of placement (estimate 2-4 months per year) and training within Baker Hughes.

Baker Hughes is a top-tier oilfield service company with a century-long track record in delivering solutions that help oil and gas operators make the most of their reservoirs. A history of technology innovation is a cornerstone of their success. Local teams are supported by global centres of excellence where scientists push the boundaries of value-adding technology to find solutions for progressively more complex technical challenges. Baker Hughes have a distinguish track record of academic engagement reflected through its strategic Alliance with Heriot-Watt University.

Baker Hughes will provide technical support and continuous professional development resources to this project beyond the studentship funding, including: technical supervision, access to historical data and design of assets, lifecycle test facilities, in-situ monitoring of assets and technical training. The engineering expertise and test facilities of Baker Hughes are critical assets to the program of research that students are expected to fully utilise.

Applicants should

Have a first class honours or good upper second class degree in Mechanical Engineering, Electronics, Materials Science, Computer Science, Mathematics or Embedded Systems or equivalent
With respect to studentship (1) the candidate should have an understanding of statistical methods of analysis, data acquisition, data processing and modelling.

With respect to studentship (2) the candidate should have an understanding and experience of Multiphysics software and ideally FMMEA.

Candidates should demonstrate a keen interest in FMMEA, prognostics, sensors and systems engineering. Working across research teams within academia and industry the candidates will require good organisational and communication skills.
Experience or knowledge of design methodologies is desirable but not essential.
Meet the minimum English Language requirements, details available on the website
Satisfy the UK residency requirement – see EPSRC eligibility criteria

How to Apply

Applications should be made on the EPS Postgraduate Applications Page

We offer a unique 4 year full-time programme which enables students to develop their research skills whilst working with industrial partners. Research training is also complemented by non-technical subjects e.g. leadership, enterprise and social responsibility. Our researchers will be at the forefront of the latest developments in Embedded Intelligence and be supported by over 45 academic members of staff and industry specialists in this field

Embedded Intelligence is the integration of intelligence into products, processes and services so they work better and can increase productivity, efficiency and connectivity. It is underpinned by diverse areas of expertise (engineering, mathematics, materials, manufacture design and computer science).

Informal enquires about the research projects should be made to Dr David Flynn. Enquiries about the application process and CDT programme can be made to Dr Flynn or visit the website: www.cdt-ei.org.

James Watt Scholarships

As part of an ambitious expansion programme to intensify further our world-leading research programmes, Heriot-Watt University is offering a fourth round of James Watt Scholarships in the School of Engineering & Physical Sciences.

The James Watt scholarships will provide full fees and stipend for 3 years from Autumn 2017.

James Watt Scholarship projects leading to a PhD in Electrical Engineering

Heriot-Watt University has now created additional Doctoral Training Partnerships and James Watt Scholarships in the School of Engineering & Physical Sciences for 2017. The James Watt scholarships will provide full fees and stipend for 3 years from Autumn 2017, whilst the DTPs provide full fees and stipend for 3.5 years.  These scholarships are described below.

Requirements

All applicants must have or expect to have a 1st class MChem, MPhys, MSci, MEng or equivalent degree by Autumn 2017.  Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or by Skype).  DTP’s are only open to UK/EU applicants.

Level of Award

For James Watt Scholarship students, the annual stipend will be £15k and full fees will be paid, for 3 years, whilst for DTP Scholarship students, the annual stipend will be £14,296 and full fees will be paid, for 3.5 years

JWS2017/20 : Manufacture of a bio-reactor for the production of artificial spider silk

Supervisor: Prof. M. Desmulliez, email: m.desmulliez@hw.ac.uk

JWS2017/21 : Microwave and antenna technology for satellite communications

Supervisor: Prof. G. Goussetis, email: g.goussetis@hw.ac.uk

JWS2017/22 : Acoustic Sensor Networks for subsea extreme environments

Supervisor: Prof. Y. Petillot, email: y.r.petillot@hw.ac.uk

JWS2017/23 : Bio-inspired Additive Manufacturing for 3D printed multimaterial integration

Supervisor: Dr. J. Marques-Hueso, email: J.Marques@hw.ac.uk

JWS2017/25: Advanced Signal Processing for Compact Deployments of Massive-MIMO Wireless Systems

Supervisor: Dr. M. Sellathurai, email: M.Sellathurai@hw.ac.uk

How to apply for James Watt Scholarship

For more information about how to apply, please apply online.

BASP Group Positions

Research associate and PhD positions are available with the Biomedical and Astronomical Signal Processing (BASP) group. Please refer to the BASP webpage.

Vacancies: Research Fellow/Post-Doc Positions available, please contact M.Sellathurai@hw.ac.uk if you are interested in PhD studentships and postdoctoral fellowships with the group in the area of signal processing for communications research.

EPSRC Industrial CASE PhD Studentship

Adaptive Automatic Assessment for Engineering Education and Training

Heriot-Watt University and ARM Ltd

Opportunity

A fully funded PhD studentship is immediately available within the School of Engineering and Physical Sciences at Heriot-Watt University in conjunction with ARM Ltd. The research will be targeted at adaptive automatic assessment for engineering education and training and will focus on supporting on-line training materials. The successful candidate will be provided with funding to pay tuition fees and a tax-free maintenance stipend for 4 years.

Conventional summative and formative assessment methods, relying heavily on human intervention, do not scale up readily to the needs of mass-participation on-line education and training in, for example massive open on-line courses (MOOCs). This project seeks to develop and demonstrate ways in which significant parts of such assessment can be handled automatically by adaptive software tools that harness the most recent advances in machine learning. Embedded systems design and programming, using ARM development tools, will be used as a demonstrator area with ARM's latest educational and professional training programs as test vehicles. The resulting tools will be tested through large worldwide field trials. If successful, the work will be expanded to other areas of education.

Requirements

We are seeking a talented, creative, proactive, and strongly motivated individual to work on a new ground-breaking project with the potential for publications in high impact journals. Suitable applicants will have a good first degree in Computer Science Electrical and Electronic Engineering or other relevant subject in engineering or the physical sciences. Only candidates with good communication skills in English, including oral and written language are encouraged to apply.

Key requirements include -

Good programming skills in C/C++ or equivalent
Experience in machine learning and web technologies is desirable
Experience in embedded systems’ design and programming is desirable

The selected student will be based at the School of Engineering and Physical Sciences at Heriot-Watt University in Edinburgh. They will also work for periods of a week at a time, typically four times per year, at ARM in Cambridge (travel and accommodation provided). There will be opportunities to travel to conferences and other events for collaboration and dissemination purposes.

How to apply

In the first instance applications should consist of a cover letter and a CV containing a detailed description of the candidate’s most relevant experiences and expertise in the fields of computer-based learning, machine learning and/or microcontroller applications and technology. A brief paragraph about the candidate’s extracurricular activities is also welcome.

Please note that this studentship is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more.

All documents should be sent to Dr Donald Reay (D.S.Reay@hw.ac.uk)

Further information

https://www.hw.ac.uk/schools/engineering-physical-sciences/

http://arm.com/