Last application date
    Oct 15, 2020 17:37
Department
    TW06 - Department of Electronics and Information Systems
Employment category
    Doctoral fellow
Contract
    Limited duration
Degree
    Master of science in computer science, computer science engineering, cognitive sciences
Occupancy rate
    100%
Vacancy Type
    Research staff

Job description

In the recently approved Marie Skłodowska Curie Innovative Training Network “SmartNets”, 7 PIs from 5 universities work together with 6 partner organisations to understand network computations, with a focus on biological or biologically inspired neural networks (see https://www.smartnets-etn.eu/).

The behaviour of a network is critically determined by its structure. This structure induces collective emergent behavior that can only be understood by analysing the whole network in relation to its constituent parts. Particularly in the brain, the architecture of the network is essential for its functioning: information transfer and processing within and between networks.

The relation between network structure and information processing is essential. Only recently, with high-throughput techniques, have we begun to collect the vast amounts of data needed to study the structure and functioning of biological networks. However, analysing these data is still a challenge and the nature of complex network processes is still poorly understood. In order to compare networks, e.g. simulated versus physical, or healthy versus diseased, network analysis and visualization tools are needed across three analysis dimensions: structure, activity and information processing.

At IDLab (Ghent University - imec, https://www.ugent.be/ea/idlab/en) we have two positions within this project. One is more focused on modelling and analysis, while the other os more focused on bringing brain-like computation and learning into artificial and embedded neural networks.

Position 1:

In this PhD project, you will investigate the specificity of the relationship between local/global topology and network activity/computation. You will use and develop measures of graph topology and graph similarity for biological networks and quantify the sensitivity of network activity and information processing to variations in these measures, or in other words, how well ‘abnormal’ behaviour can be explained based on variations in these structural network measures.

Position 2:

In this PhD project, you will study how the local topology and local neural adaptation mechanisms in biological networks affect the global network behaviour and the efficiency with which the network can be optimised (learn) to perform the desired behaviour. In view of developing future biologically inspired computing algorithms and systems, you will also evaluate how biologically inspired local learning mechanisms can be efficiently implemented in analog or digital hardware.

We offer an interesting and interdisciplinary research position in a large and international research group at Ghent University. Our team has a long standing history in various types of brain-inspired computing, as well as state-of-the-art machine learning and deep-learning.

Profile of the candidate

We are looking for two full-time PhD students at Ghent University. Together with 12 other international PhD students in 5 different countries, you will work within the Marie-Curie ETN network SmartNets (www.smartnets-etn.eu). The project also requires you to spend a number of months as a visiting researcher with one of our partners (which partner depends on your specific research project).

You must be a junior researcher and you may not have resided or carried out your main activity (work, studies, etc.) in Belgium for more than 12 months in the 3 years immediately before the recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention, are not taken into account.

Your initial contract will be for one year. Upon positive evaluation, this can be extended to at most 3 years (limitation set by the project).

Job requirements:

Master of science in computer science, informatics or cognitive or computational neuroscience with a strong interest in brain-inspired computing and adaptation and learning in biological or artificial neural networks. Skills: Good programming skills, a decent background in machine learning, fluent spoken and written English, good social skills.

How to apply

Your application should contain:

    A motivation letter that clearly states which of the vacancies you are most interested in and why you think you are a suitable candidate.
    A complete CV, including copy of diploma and course grades list
    For non-European candidates that are not native English speakers: a certificate or other proof of proficiency in English

Incomplete applications or applications that are too generic will not be considered.

After an initial screening of your application, you will either be invited for further assessment (starting with a video interview) or notified that your profile does not match what we are looking for.

dpo@UGent.be