PhD opportunities
BENS is currently looking for HDR students in the following projects:
- Analogue VLSI and Neuromorphic Engineering
- Stochastic Integrated Circuit Design
- Bayesian Inference in Spiking Neural Networks
- Event based sensors and event based processing
- Simulating brains
- Astrocytic regulation of neuronal oscillations
- The impact of neuroinflamation on the cholinergic system
- Human Physiology Monitoring and Disease Diagnosis using Wearable Technologies
- Balance Rehabilitation and Treatment using Wearable Technologies
- Monitoring chest Haemodynamic and lung-heart interactions
- Electrocardiography data mining
- Integrated physiological signal amplifier with digital output
Opportunity title:
Analogue VLSI and Neuromorphic Engineering
Description:
Neuromorphic engineering is a multi-discipline approach to building electronic systems that are inspired by biology and it aims to replicate many of the tasks that biological systems excel at. We will implement electronic circuits for sensory and neural signal processing. Specific examples of such chips would be a silicon cochlea emulating the filtering performed by the cochlea in the ear, or an IC containing a network of spiking neurons to perform computations based on how we think the brain processes sensory signals.
Requirements: VLSI design, analogue VLSI preferred. An interest in how the brain works is essential.
Supervisor:
Prof André van Schaik
Opportunity title:
Stochastic Integrated Circuit Design
Description:
Shrinking feature sizes of modern IC processes have come at the cost of increased mismatch, susceptibility to noise and large variations between individual dies and wafers. In order to successfully integrate analogue circuits as well as increase speed and improve energy efficiency of digital circuits on modern processes new design techniques, design topologies and design methodologies are needed. In this project we'll explore various techniques from stochastic signal processing in order to achieve better reliability and performance in modern IC processes.
Requirements: 1+ years experience in IC design using Cadence or equivalent, FPGA design skills (pref. Xilinx), Matlab/Python programming, PCB design skills (e.g. Altium), excellent written and verbal communication skills.
Supervisor:
Prof André van Schaik
Opportunity title:
Bayesian Inference in Spiking Neural Networks
Description:
The brain creates a coherent interpretation of the external world based on input from its sensory system. Yet data from the senses are unreliable and confused. How does the brain synthesise its percepts? Recent psychophysical experiments indicate that humans perform near-optimal Bayesian Inference in a wide variety of cognitive tasks, such as motion perception, decision making, or motor control. In Bayesian Inference – a powerful mathematical framework – the likelihood of a particular state of the world being true is calculated based not only on sensory input signals but also on prior knowledge about the external world that the system has already learned. The Bayesian framework has also been shown to be ideal for fusing information from different sensory modalities and is robust to errors in individual sensors.
Neurones in the brain use action potentials (spikes) to communicate with each other. From calculations based on the energy consumption of the brain, it has been estimated that, on average, each neurone fires only one spike per second, although individual sensory neurones can fire close to 1000 spikes per second. The question of how Bayesian Inference can be implemented using spiking neurones with such slow communication rates is intriguing. In the past five years a dozen of papers have been published showing glimpses of how this could be achieved. This project will extend the work in these papers.
Requirements: Programming in C++ and Matlab or Python. An interest in how the brain works is essential.
Supervisor:
Prof André van Schaik
Opportunity title:
Event based sensors and event based processing
Description:
Biological sensors and neuromorphic sensors provide information about the sensed variables in the form of discretised events (digital spikes) in an asynchronous manner – they are not aligned to a global clock signal. Well-developed analogue signal processing techniques operate on continuous signals in both amplitude and time, whereas even better developed digital signal processing techniques operate on signals discretised in both amplitude and time. Neither of these standard signal-processing approaches apply optimally to the output of neuromorphic sensors and there is a large gap in our knowledge of spike based signal processing techniques. This PhD project aims to address this gap.
Requirements:
The applicant is expected to have a background in signal processing with strong programming skills in Matlab, Python, and C++, and an interest in signal processing in biology. Expertise using Verilog or VHDL for FPGA programming will be beneficial.
Supervisor:
Prof André van Schaik
Opportunity title:
Simulating brains
Description:
We are getting to the point where neuromorphic hardware is able to simulate spiking neural networks on a scale comparable to the human brain. For example, in our lab we have recently simulated 10 billion Stochastic Leaky-Integrate-and Fire neurons with 1300 synaptic connections each in real time with a sub-millisecond resolution. Several global companies and universities are building hardware for this purpose. The main problem with this is that nobody seems to know what to do with such large networks. Theoretical and computational neuroscience so far has not provided us with any models of how to use these large-scale spiking networks advantageously. In other words, anything existing models can do can be done much more simply with standard engineering approaches. At the same time, everybody agrees that brains are remarkable computational devices that do things that computers cannot. Thus there seems to be a gap in our knowledge on how to simulate brain-scale networks of spiking neurons and what to use them for. This PhD aims to address this gap.
Requirements:
The applicant will have a background in signal processing or computational neuroscience, with several years of relevant experience post Masters degree, either in academia or industry. The applicant should have excellent programming skills in Matlab, Python, and C++, and an interest in neural systems. Expertise using Verilog or VHDL for FPGA programming will be beneficial.
Supervisor:
Prof André van Schaik
Opportunity title:
Astrocytic regulation of neuronal oscillations
Description:
The rhythmic voltage fluctuations generated through the synchronous activity of neuronal networks are thought to be involved with many physiological processes such as selective attention, sleep and memory. The aim of this project is to investigate the potential role for astrocytes, which are the most prevalent non-neuronal cell type in the brain, in mediating the transition between different frequencies of these oscillations. Furthermore, this study will provide important insights into the bi-directional communication that astrocytes establish with neurons, which is one of the most intriguing questions in neurobiological research today.
Requirements: BSc Honours degree in Neuroscience, Biology or Physiology.
Supervisor:
Dr Yossi Buskila
Prof John Morley
Opportunity title:
The impact of neuroinflamation on the cholinergic system
Description:
Alzheimer's disease (A.D) is a neurodegenerative disorder characterized by significant impairment of cognitive function, memory loss and behavioural phenotypes such as anxiety and depression. According to the cholinergic hypothesis, a serious loss of cholinergic function in the CNS contribute significantly to the cognitive symptoms associated with AD. Recent findings suggest that neuroinflammation is a preliminary process, which play a role in the onset of Alzheimer's disease. This project will investigate the effects of chronic and acute inflammatory processes on the neurophysiological properties of the basal forebrain cholinergic system, a region that is associated with A.D.
Requirements: BSc Honours degree in Neuroscience, Biology or Physiology.
Supervisor:
Dr Yossi Buskila
Opportunity title:
Human Physiology Monitoring and Disease Diagnosis using Wearable Technologies
Description:
The ability to monitor human function is key in the early detection of disease and effective treatment once diagnosed. However, many imaging and monitoring solutions are cumbersome, costly and do not provide the kind of information we would like to have (e.g. continuous blood pressure monitoring). PhD topics in this area would centre around providing patient-centred, pertinent, high value information from novel sensors integrated into everyday clothing.
Requirements:
We are a multidisciplinary team and seek candidates with a Bachelor Honours degree from a variety of backgrounds that can contribute to this research. Additionally, the following skills would be beneficial: Programming skills in Matlab, C++ or Python. Analogue/digital/embedded design experience. Background in physiology, neuroscience or textiles.
Supervisor:
Dr Paul Breen
Opportunity title:
Balance Rehabilitation and Treatment using Wearable Technologies
Description:
Impairment in balance is common due to aging, disease and trauma. Poor balance can lead to significant reductions in quality of life, for example, the ability to drive a car or live independently. Ongoing research in this area is literally head to toe; some work attempts to improve inner ear function using electrical stimulation while other work augments balance feedback from the feet.
Requirements:
We are a multidisciplinary team and seek candidates with a Bachelor Honours degree from a variety of backgrounds that can contribute to this research. Additionally, the following skills would be beneficial: Programming skills in Matlab, C++ or Python. Analogue/digital/embedded design experience. Background in physiology, neuroscience or textiles.
Supervisor:
Dr Paul Breen
Opportunity title:
Monitoring chest Haemodynamic and lung-heart interactions
Description:
Heart and lungs share the same cavity and affect each other function. Disruptions of the respiratory patterns (i.e. sleep disordered breathing) can trigger or aggravate cardiovascular diseases. Similarly, cardiac diseases (i.e. cardiac insufficiency) can trigger or aggravate respiratory conditions. Unfortunately current diagnostics are limited as they are designed to give a snapshot of single organs often in resting conditions. In this project the PhD student will develop a new system that will assess simultaneously cardiac and lung functions targeting specific diseases such as obstructive sleep apnoea, maternal disordered breathing and cardiac failure. This will involve the design and development of custom hardware and software. It will also involve collaboration with clinicians for the validation of the developed system.
Requirements:
Programming in C++ and Matlab. Analogue/digital/embedded design experience.
Supervisor:
Dr Gaetano D. Gargiulo
Dr Paul Breen
Opportunity title:
Electrocardiography data mining
Description:
Electrocardiography (ECG) is probably the most recorded and studied biosignal. However, the data are severely undermined with a number of unmet needs such as data expansion/compression and automated diagnosis of diseases. In this project the PhD student will develop new data analysis algorithm as well as new recording hardware suitable for reduced electrodes montages (hardware compression) and wearable applications. This will involve the design and development of custom hardware and software. It will also involve collaboration with clinicians for the validation of the developed systems.
Requirements:
Programming in C++ and Matlab. Analogue/digital/embedded design experience.
Supervisor:
Dr Gaetano D. Gargiulo
Opportunity title:
Integrated physiological signal amplifier with digital output
Description:
Physiological signal amplifiers i.e. amplifiers suitable for Electrocardiography (ECG), Electroencephalography (EEG) and Electromyography (EMG) must comply with strict safety standards, time-dependant high impedance electrodes, and different input configuration requirements (true differential and single-ended) which may vary during recording. Commercially available integrated circuits, however, are tailored to specific applications and electrode montages. Furthermore, they are not compatible with neuromorphic, event-based signal processing. In this project we investigate the feasibility of a tiny integrated analogue front-end (AFE) with direct digital output suitable for standard digital bus communication (SPI, I2C) compatible with both standard logic circuits and neuromorphic, event-based signal processing. The PhD student will design a fully integrated AFE which is fully configurable and compatible with neuromorphic signal processing, taking advantage of the ultra-low-power amplifier technology and neuromorphic, event-based processing front-ends that have been developed within BENS in recent years.
Requirements:
1+ years experience in IC design using Cadence or equivalent, FPGA design skills (pref. Xilinx), matlab/python programming, PCB design skills (e.g. Altium), excellent written and verbal communication skills.
Supervisor:
Dr Gaetano D. Gargiulo
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