PhD in Algorithmic Approximate Computing
The aim of this PhD project is to develop techniques for performing approximate computation in energy-constrained environments. Although battery lifetime has increased significantly over the last years, the most sophisticated data processing algorithms require so much computation that they cannot be deployed in operations using battery-powered drones or sensor networks. Such massive computation would indeed compromise the longevity of the operation. A solution is then to approximate the algorithms by decreasing the amount of computation, which can be done at the algorithmic level (e.g., by the selection of a more efficient optimization algorithm), all the way down to the hardware level (e.g., by reducing voltage).
This project will focus on the approximation aspects at the higher-levels, including the consideration of approximation at the problem formulation, the design of resilient optimization algorithms, and the development of approximate linear algebra. The goal is not only to design approximation algorithms, but also to analyze how approximations introduce errors and how these, in turn, affect the algorithm's performance and power savings. The student will look at two classes of algorithms for imaging tasks, such as classification, reconstruction, or super-resolution. One class of algorithms is based on convex optimization principles, for which there exist comprehensive performance analyses. The other class is based on nonconvex optimization, including algorithms used to train/update neural networks, for which performance characterizations are quite limited.
This project will be supervised by Dr. Joao Mota, Prof. Mathini Sellathurai, and Prof. Andrew Wallace, and will take place at Heriot-Watt University, Edinburgh.
The student will work on the University Defence Research Collaboration (UDRC) (www.mod-udrc.org), which is a leading research partnership for signal processing for defence and develops new techniques to better transform data across many domains into actionable information, and meet the requirements for improved situational awareness, information superiority, and autonomy. This collaboration, sponsored by Dstl and the EPSRC, is academia-led and has commenced its third phase of research focusing on "Signal Processing in the Information Age". The Consortium is made up of the University of Edinburgh, Heriot-Watt University, Queen's University Belfast and University of Strathclyde and there are currently PhD opportunities available across the four universities to work on diverse topics in signal processing, as part of a collaborative team. The work will involve strong links with industry and the UK defence sector. The PhD student will be expected to work closely with other research team members and to attend regular meetings to present project updates to the sponsors and partners of this project.
Candidates should have completed, or expect to complete, an MSc degree in Electrical Engineering, Mathematics, Computer Science, or similar. A strong mathematical background is essential and knowledge of optimisation algorithms, linear algebra, algorithm analysis, or machine learning is a big plus. Candidates should have an autonomous and proactive working style, and good communication skills.
The studentship is available for 3 years, starting from September/October 2019. There will be an annual stipend of around £15k, and no nationality restrictions.
To apply, please send a CV, a cover letter, and contact details of at least two referees to Dr. Joao Mota (firstname.lastname@example.org) and to Prof. Mathini Sellathurai (email@example.com), quoting "PhD in Algorithmic Approximate Computing" in the email subject. Informal queries can also be addressed to Dr. Mota.
Scalable methods for robust uncertainty quantification
Supervision team: Y. Altmann, Y. Wiaux, S. McLaughlin
Host institution: Heriot-Watt University – School of Engineering and Physical Sciences
This PhD project will investigate novel statistical methods combining the Bayesian formalism with high-dimensional optimization tools to infer parameters and associated uncertainty in large-scale (imaging) problems.
Future defence applications will involve challenges such as: the imaging, monitoring and analysis of underwater terrain and assets from heterogenous data: e.g. sonar and underwater Lidar; or the quantification of radioactive sources or pollutants from event-data, time-of-flight (ToF) data and spectral measurements. These are examples of severely ill-posed inverse problems with potentially multi-modal data where advanced signal models coupled with quantification of uncertainty will be instrumental in aiding signal and image recovery. This project will focus on solving such inverse problems, enhancing optimisation with post-optimisation confidence estimates. Among the different applications mentioned above, the proposed methodologies will be applied in particular to imaging and sensing applications in complex environments.
Convex and nonconvex optimisation tools offer the potential to enable fast scalable robust Bayesian inference in high dimensions. Proximal calculus algorithms rooted in optimisation as well as random data selection approaches and approximate Bayesian computation (ABC) to accelerate Monte Carlo sampling will be the focus of this work. Using proximal optimisation algorithms and ABC to explore the confidence region defined by Bayesian models around initial point estimates offers a route to scalable uncertainty quantification for large scale inverse problems but work is required to achieve this and to expand it to deal with multi-modal data. The issues of parallelisation and distribution functionalities, as well as the convergence properties of these proximal methods must also be addressed to enable scalable solutions to be developed.
The University Defence Research Collaboration are pleased to invite applications for PhD studentships to work as part of a leading team of experts in signal processing. The project will be hosted by Heriot-Watt University and the student will work on the University Defence Research Collaboration (UDRC). The UDRC is a leading research partnership for signal processing for defence and develops new techniques to better transform data across many domains into actionable information, and meet the requirements for improved situational awareness, information superiority, and autonomy. This collaboration, sponsored by Dstl and the EPSRC, is academia-led and has commenced its third phase of research focusing on "Signal Processing in the Information Age". The Consortium is made up of the University of Edinburgh, Heriot-Watt University, Queen’s University Belfast and University of Strathclyde and there are currently PhD opportunities available across the four universities to work on diverse topics in signal processing, as part of a collaborative team. The work will involve strong links with industry and the UK defence sector. The PhD student will be expected to work closely with other research team members and to attend regular meetings to present project updates to the sponsors and partners of this project.
For more information please contact: Y.Altmann@hw.ac.uk
2D and 3D Computational Imaging Through Turbulence
The main objectives of this computational PhD project are to develop new and ground-breaking methods for fast and robust 3D imaging and sensing. This ambitious project will concentrate on real-time solutions for analysis of long-range 3D scenes (object detection and 3D reconstruction) in the presence of scattering media and atmospheric turbulence.
Within the School of Engineering at Heriot-Watt University (HWU), the signal/image processing and optics research groups (http://www.single-photon.com) are working together to push the frontiers of 3D imaging. In particular, the high sensitivity of photodetectors allows for the use of low-power laser sources (ranging from the visible through infrared frequencies), a key element for defence applications and resource management in embedded systems. Similarly, gated-cameras, which are less expensive and potentially allowing faster acquisitions can also be used.
Among the wide variety of 3D imaging applications, long-range imaging in the photon-limited regime is of prime interest for Leonardo. Long distances (several kilometres) exacerbate the nuisance effects of light propagating through scattering media (e.g., fog) and atmospheric turbulence (e.g., wind) which in turn produce signal distortion and additional sources of uncertainty that require new algorithms to be developed. While the most recent reconstruction methods provide very good reconstructions under mild turbulence and scattering, they still require prohibitive computing times. Preliminary results (publications under review) have demonstrated the benefits of massively parallelisable algorithms allowing unprecedented frame rates while ensuring state-of-the-art estimation performance and this PhD project will continue in this direction of research. While scalable/parallelisable algorithmic structures will be adopted for fast information extraction, they will be embedded within a statistical/Bayesian framework, allowing rigorous uncertainty management tools. This is particularly relevant to account for the uncertainty and variability of data acquired in the presence of scattering media (fog/aerosols) and atmospheric turbulence.
The PhD student will be primarily based on the Edinburgh campus of HWU and will join the ISSS institute and the research groups of Dr Y. Altmann and Prof. S. McLaughlin. This project will be conducted in collaboration with the HWU IPAQS institute (Prof. G. Buller) and Leonardo.
Requirements for this project
Excellent background in statistics, Bayesian inference and computational imaging and data science
Excellent programming skills (Matlab, C++), parallel programming desired
Experience with optical physics desired
The PhD project, funded in part by Leonardo (https://www.uk.leonardocompany.com) and CENSIS (https://censis.org.uk) is available for 3.5 years preferably for UK and EU nationals. This funding includes stipends of ~£14.5k per year and full tuition fees covered for 3.5 years.
To apply, please send your CV, academic transcripts and a cover letter explaining your motivation/interest in this project, to Dr Yoann Altmann (Y.Altmann@hw.ac.uk)
Two month Internship in assistive technology at Heriot-Watt University
We’re looking for an enthusiastic and creative person to help us design and develop a sensorised punchball for Better:Gen (www.bettergen.co.uk).
The job is full-time for 2 months – at 9.5£ per hour for 37.5 hours per week, starting immediately.
Better:Gen specialises in motivating the mind through movement. Staff are qualified nurses with a passion for promoting independence, encouraging new experiences and skills and helping individuals live life to the fullest no matter how young or old or their physical ability
The punchball 'BoB' was introduced by Better:Gen as a way of promoting movement among older people with mental health issues such as dementia in a fun way.
‘BoB’ has also demonstrated its applicability with individuals with autism, ADHD, stroke etc, and as a tool to connect young and old within intergenerational groups.
The intern will collaborate with Better:Gen and researchers at Heriot-Watt University (Institute of Sensors, Signals and Systems) and at the University of Stirling, to fit 'BoB' with sensors, lights, microphones and wireless connectivity (e.g. Bluetooth) to enable to customise its functions and to enable monitoring of individual progression through a mobile app (e.g. for Android phones).
It is expected that the final prototype will be a new and highly innovative piece of therapeutic equipment.
Is this job for you?
Do you have experience with sensors, and embedded and mobile programming?
Are you keen to gain experience in assistive technology?
Are you full of creativity?
Do you love looking at a problem and finding an interesting way of solving it?
Do you want to contribute to provide real and measurable benefit to individuals with physical and/or mental challenges?
If you answer yes to these questions, then apply now by writing to firstname.lastname@example.org
Electromagnetic Sensors for Wearable Healthcare Applications
Wearable electronics technology for healthcare management, personal safety, and consumer products enhancement has the potential to transform our everyday life and improve the quality of living of healthcare patients and athletes. Non-invasive healthcare monitoring of body signal bio-parameters – such as movements, respiration, and temperature – without physical intervention or interaction with the patient is particularly of interest in this project. To date, the widespread adoption of many body wearables is limited due to intrinsic limitations mainly related to the flexible/rigid interface: complex wiring, mechanical/electrical reliability, presence of rigid and bulky batteries and charging circuits, washability. Seamless integration is key for user convenience that will ultimately lead to adoption of the technology in everyday applications.
This PhD research project envisages developing a wireless body sensor reader that can detect and estimate human body signals through a passive radio-frequency interrogation process and will be integrated with a sensing antenna. It is expected that this PhD research project will change the conventional approach to wearable healthcare electronics, ultimately leading to novel RF circuit architectures that take advantage of modern System-on-a-Package and System-on-a-Chip technology developments, and it may be a key enabler in lowering healthcare costs, particularly for the elderly.
Eligibility: DTP (UK nationals, or EU citizens who lived in the UK for the past 3 years)
Interested potential PhD candidates, please contact Prof. Dimitris Anagnostou for enquires
Downhole Wireless RF Communications for Deployment in Oil and Gas Wells
There is an industry desire to create and extend the usage of RF wireless technologies to develop future digital-based oil fields. In particular, this PhD project will assess current evolving aspects of wireless technology to be adapted for providing sub-surface monitoring of Oil and Gas wells. Currently most well measurement communications systems employ permanently installed electrical cables or acoustic technologies.
The intention of this project is to assess and model the physical wellbore architecture and EM environment for the application of wireless RF communications in shallow sub-surface well environments; initially identifying the potential transmission schemes and then developing a wireless RF communications system (early prototype) to prove and validate the novel concept. The prototype, once developed, is patentable and will involve the development of new antennas and RF components as well as the system modeling, design, and implementation. At least two IEEE publications are expected from this research as well as conference publications and travel. This engineering research also has the opportunity to deliver low cost well performance for monitoring well head operational efficacy and crude throughout. Applications are expected for the Global Oil & Gas market sectors. Moreover, this proposed wireless well communications modem system can benefit the Global Oil and Gas industry by reducing the high cost of well interventions ($1 million / well) and can provide a key enabler for deploying low cost well measurement technology in wells and further allow for the introduction of new wireless RF sensor technologies.
In addition to this cost savings, there are significant benefits to production optimization by measuring more wells on a continuous basis. It is expected that this PhD research project, to develop the first RF wireless modem system for wells in the Global Oil and Gas Industry, will change the conventional approach to well measurements and monitoring. This can deliver a cost effective technology solution to enable the monitoring of more wells on a continuous basis. Ultimately this RF communications technology implementation can lead to the lowering of the number of routine well interventions, and, it may be a key enabler to lowering crude costs. In addition, the proposed wireless RF sensor communication system may foster the permanent monitoring of Oil and Gas wells with enhanced efficacy.
Interested potential PhD candidates, please contact Prof. Symon K. Podilchak,for enquires
Algorithm Design for Energy Demand-Side Response
Heriot-Watt University and the University of Edinburgh are pleased to offer a fully funded PhD position on using artificial intelligence, machine learning and distributed optimization techniques for distributed demand side response.
In more detail, the PhD project involves looking the problem of aggregating a large pool of energy consumers and storage devices to be able to respond in real time to frequency dips and price signals from the grid and distributed system operators. The project will have both a theoretical and practical component. The theoretical component will explore using of the latest techniques from multi-agent systems, machine learning and distributed optimization to coordinate, control and reward a pool of devices with different capabilities. These range from buildings energy use (both office and residential), to industrial devices (such as refrigerators) to storage devices such as batteries and electric vehicles.
The practical component will involve a close collaboration with Upside Energy. Upside Energy is an innovative energy start-up, with offices in Manchester and London, which enables consumers to make smart choices about when to use energy. Upside is a UK leader in the field of distributed energy demand response, in which consumers and storage devices are paid to reduce their consumption or inject power in the grid at time of peak demand. It has developed an Advanced Algorithmic Platform (AAP) which allows the decentralised control of a large number of storage devices performing DR.
Interested potential PhD candidates, please contact Prof. Symon K. Podilchak for enquires.
Candidate profile and requirements: The candidate should have a good degree in either computer science or electrical engineering, with a background in distributed AI, optimisation or algorithm design, and an affinity with energy systems. Graduates from electrical engineering with a background and interest in data analysis are also encouraged to apply. For UK candidates, a 1st class or a good 2:1 first degree or a relevant MEng or MSc degree is required. Candidates from EU countries with equivalent MSc degrees and experience are also strongly encouraged to apply.
The position is fully funded for 3.5 years, including full tuition fees for UK and EU candidates, with a tax-free stipend of £15,000/year and a generous annual budget for international conference travel, training and equipment.
About the team & contact information: The candidate will be co-supervised by Dr. Valentin Robu (Heriot-Watt University), as 1st supervisor and Dr. Aristides Kiprakis (University of Edinburgh) as 2nd supervisor. She/he will participate in joint activities, such as the Edinburgh smart grid journal club, as well as participate in some of the activities of Energy Research Partnership (Scotland), including presenting research results at their annual conference. Moreover, they will also work closely with the team at Upside Energy, the wider collaboration between the institutions includes another postdoctoral research associate.
The Smart Systems Group at Heriot-Watt University in Edinburgh is an interdisciplinary research group that spans research ranging from electrical engineering and energy systems to computer science and artificial intelligence. The general engineering submission of Heriot-Watt and the University of Edinburgh was ranked first in the UK in terms of research power in the general engineering category in the most recent UK REF assessment.
Inquiries should be directed at Dr. Valentin Robu . Application deadline is 10th December, but applications will be accepted on an ongoing basis until post is filled.
PhD in Signal Processing Techniques For Biological Imaging
The aim of this PhD project is to develop new imaging techniques for biological microscopy. One of its components is the design of signal processing and machine learning algorithms that super-resolve cell images using as an aid images obtained in a different range of the spectrum. Specifically, some microscopes are equipped with two cameras, which may capture different ranges of the spectrum: one camera has high spatial resolution but low temporal resolution, while the other has low spatial resolution but high temporal resolution. Our goal is to use post-acquisition processing to boost the spatial/temporal resolution of both cameras.
Another component of the project concerns single-photon avalanche photodiode (SPAD) array cameras, which work by taking a large number of measurement images from a given biological setting. Each measurement image, however, is extremely sparse, and therefore the biological setting can be reconstructed only if we use a large quantity of measurements. A recurrent problem in practice is that there are no estimates for how many measurements are required for successful reconstruction. One of the goals of this project is precisely to design algorithms that perform reconstruction from SPAD measurements and to establish estimates on the number of measurements that they require.
This project requires a strong mathematical background, but no prior experience in biological imaging is expected. Candidates should have completed, or expect to complete, an MSc degree in Electrical Engineering, Mathematics, Computer Science, or similar. Knowledge of optimization algorithms, compressed sensing, and machine learning is a plus. Candidates should have an autonomous and proactive working style, and good communication skills.
Due to funding restrictions, only UK or EU students are eligible for this studentship. The studentship is available for 3 years, with a tax-free stipend of £14,553 p.a., and start date February 2018.
To apply, please send a CV, a cover letter, and contact details of at least two referees to Dr Joao Mota and to Dr Colin Rickman quoting "PhD in Signal Processing Techniques For Biological Imaging" in the email subject. Informal queries can also be addressed to Dr Mota.
PhD Project in the Area of Sensor Manufacture - Novel Lactate/pH Sensor for Monitoring of Baby Delivery
Project Description: Currently 300,000 pregnant women a year in the UK have their babies monitored during labour using cardiotocography (CTG), as recommended by the NICE intrapartum care guideline. It is however recommended that fetal blood sampling, where a sample of blood is taken from the baby’s scalp, is used in conjunction with CTG monitoring to confirm the acid-base and lactate balance of the fetus. Unfortunately current methods of taking fetal blood samples are time-consuming, technically difficult, unreliable, and require the use of an expensive separate blood gas analyser. They also only give a snap-shot of the state of fetal wellbeing at the time of sampling and do not provide a continuous real-time assessment of fetal wellbeing. There is therefore an unmet clinical need for a device capable of providing accurate real-time assessment of fetal acid-base and lactate state though-out labour to inform timing of delivery and prevent brain injury.
We are looking for a creative and highly motivated student willing to work in the field of sensor manufacturing in collaboration with our colleagues from the University of Edinburgh and the Charity Tommy's. Studies in Chemistry, Physics, or Engineering (Electrical, Mechanical, Chemical, Materials, Biomedical or related), are desired. The student is expected to work effectively as a part of a team, both in the host institution and with the project partners. Good manufacturing and/or experimental skills are required.
The student is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under its Centre of Doctoral Training (CDT) programme. This particular CDT is run in collaboration with the University of Loughborough although the PhD position will be at Edinburgh (Heriot-Watt University and the University of Edinbugh). The project consists in the development of a novel lactate/pH sensor to monitor hypoxia during the delivery of babies. The PhD will be developed under the supervision of Prof. Marc Desmulliez (Engineer at Heriot-Watt University) and Dr Fiona Denison (Obstetrician at the University of Edinburgh/NHS Lothian). The Charity organisation, Tommy's, is sponsoring part of the PhD studentship. Heriot-Watt is Scotland's most international university, boasting the largest international student cohort, with five campuses in three countries. In a strategic partnership with the University of Edinburgh, EPS was ranked first in power ratings in General Engineering by the last Research Excellence Network assessment of UK research quality (REF2014).
All applicants must be home/EU and must have or expect to have a 1st class Bachelor Degree or a MChem, MPhys, MSci, MEng, MBioChem 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). The Scholarship consists of an annual stipend of £17,000 (tax free) and full tuition fees payment, for 4 years.
Deadline of applications: 03 November 2017. The successful candidate must commence studies by early January 2018 at the very latest.
All applications should be made online by using the electronic system of Heriot-Watt University:
Improving detection and estimation of birds’ collision risk with energy infrastructure
New tracking technologies have enabled researchers to unravel previously unknown animal movement strategies and migratory routes. The use of animal tracking devices that collect high temporal and spatial precision data, including altitude information has been increasing in recent years. Altitude data is necessary to estimate birds’ collision risk with offshore wind turbines, electricity cables or other energy infrastructure. This information is key and part of the decision-making process for proposed renewable developments. Areas with high collision risk should be avoided. This project has two components, first it will examine existing animal movement data, from devices that provide accurate elevation information and will examine bird collision risk with the energy infrastructure in Europe. This is important to identify areas with high collision risk that need mitigation actions. The second part of the project will focus on the development of new technology to determine when a bird actually collides. The devices will be tested during the PhD and the results of this project will open new avenues for identification of risk of collision, an issue that is increasing in importance worldwide.
Methodology: We will use data on the movement of migratory birds from a variety of projects freely available on Movebank. Movement characteristics (altitude, speed, direction, flight type and distance moved) will be used to determine locations of high collision risk. This project will also take advantage of existing high temporal and spatial resolution data from ~80 white storks and will use newly developed features to identify collision events. The student will be involved in the development of the new features. The collision risk with energy infrastructure will be determined using GIS approaches. The student will benefit from the expertise in data analyses and field techniques provided by the supervisory team and collaborating partners.
Deadline for applications: 16th January
PhD studentship: Luminescent nanostructures for biosensing
This project targets the development of nanostructured materials to be used in sensing technologies. Special attention will be given to luminescent materials and their non-linear effects, such as upconversion, in order to create new transducers.
For this, we are looking for a creative and highly motivated student to work in the field of luminescent nanomaterials and sensing technologies. Studies in Physics, Materials Engineering, Chemistry, or related, are desired. The student is expected to work effectively as a part of a team, both in the institution itself and with external partners. A good academic record and excellent organisational and lab skills are required.
During the project, new nanostructures for sensing will be produced. For this, the student will be introduced to nanofabrication via chemical and physical routes. Luminescent phenomena will be used as a sensing principle, and optical characterisation of the materials as well as of the completed devices will be required. The student will be expected to collaborate closely with the partners developing other parts of the sensors.
All applicants must have or expect to have an MChem, MPhys, MSci, MEng or equivalent degree by autumn 2017. Good academic record and excellent organisational and lab skills are required. Candidates are required to be UK or EU nationals.
Selection will be based on academic performance, matching to the project and research potential, and all short-listed applicants will be interviewed (in person or via Skype). The student will be awarded an annual stipend for 3 years, approx. £14,553 (tax free) the first year, which will be incremented each following year, and full payment of fees in order to complete the PhD thesis.
Deadline for applications: all year round.
If you wish to discuss any details of the project informally, please contact Dr. Jose Marques-Hueso, Email: J.Marques@hw.ac.uk
All applications should be made online by using the electronic system of Heriot-Watt University:
Please attach a complete CV and motivations letter.
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.
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.