Network Analysis
network analysis
Ing. Mgr. Jaroslav Hlinka, Ph.D.
Postdoctoral / Junior Scientist position in Complex Networks and Information Theory A Postdoc or Junior Scientist position is available to join the Complex Networks and Brain Dynamics group for the project: “Network modelling of complex systems: from correlation graphs to information hypergraphs“ funded by the Czech Science Foundation. The project involves developing, optimizing and applying techniques for modelling complex dynamical systems beyond the currently available methods of complex network analysis and game theory. The project is carried out in collaboration with the Artificial Intelligence Center of the Czech Technical University. Conditions: • Contract is of 18 months duration (with the possibility of follow-up tenure-track application). • Starting date: position is available immediately. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 9. 2022. • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 42 000 - 48 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses depending on performance and travel funding for conferences and research stays. • Contribution for reallocation costs for succesful applicant coming from abroad: 10 000 CZK plus 10 000 CZK for family (spouse and/or children). • No teaching duties
Dr Panayiota Poirazi
The successful applicant will build a simulation model of the rodent visual cortex and use it to assess the role of dendritic nonlinearities on the connectivity and activity properties of the resulting memory engrams. Selected model predictions will be tested in headfixed behaving animals performing a visual discrimination task. For more information see: https://www.smartnets-etn.eu/role-of-dendritic-nonlinearities-in-v1-network-properties-after-visual-learning/
Nathalie Japkowicz
The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.
Nathalie Japkowicz
The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.
Jaroslav Hlinka
Several postdoctoral positions are available in the Complex Networks and Brain Dynamics group (COBRA) at the Institute of Computer Science, Prague (Czech Republic). These positions are part of a larger, interdisciplinary consortium that was recently recommended for funding (OPJAK BRADY). The project topics include: Topic 1: Detailed biophysical modelling of neurotransmitter action (supervised by Pavel Sanda). Topic 2: Computationally efficient mean-field models of cortical microcircuits (supervised by Helmut Schmidt). Topic 3: Whole-brain dynamics with applications particularly to schizophrenia and Parkinson's disease (supervised by Gustavo Deco). Topic 4: Data-driven model inversion and personalized parameter identification (supervised by Nikola Jajcay). Topic 5: Modelling interventions into arousal dynamics (supervised by Jaroslav Hlinka). Other related topics may be considered based on their fit to the overall project.
Roberto Interdonato
We are offering a Master's 2 internship in natural language processing on ‘Extracting knowledge about land use and land cover changes from textual data’. This internship will take place over a period of 6 months between January and June 2025 and will be co-supervised by CIRAD researchers from UMR TETIS as part of the TOSCA-CNES ARENA (Automatic Rule Extraction and Network Analysis) project.
Pavel Sanda
We offer 2 fully funded Postdoc / Ph.D. positions for the OPJAK Trust project. Our group is part of the Department of Complex Systems at the Institute of Computer Science of the Czech Academy of Sciences. The topic is focused on analysing modern and historical textual records through the lenses of graph-network analysis, statistics and machine learning. The topics include: Networks in Historical Scholarly/Philosophical Writings and Information Flows in Scholarly & Scientific Networks. Positions are available starting March 1, 2025 and later. Applications will be reviewed on a rolling basis, until the positions are filled. Contract is for 12-36 months duration, with exceptions negotiable. Possibility of extension until end of project (31. 12. 2028), support for transition to tenure-track or external funding applications. This is a fixed term contract appointment, part time contract possible. Competitive salary based on qualifications and experience. Bonuses and travel funding for conferences and research stays depending on performance. No teaching duties.
Learning the structure and investigating the geometry of complex networks
Networks are widely used as mathematical models of complex systems across many scientific disciplines, and in particular within neuroscience. In this talk, we introduce two aspects of our collaborative research: (1) machine learning and networks, and (2) graph dimensionality. Machine learning and networks. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. We have developed hcga, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. Taking inspiration from hctsa, hcga offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that hcga outperforms other methodologies (including deep learning) on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features, which we exemplify on a dataset of neuronal morphologies images. Graph dimensionality. Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. Deviating from approaches based on fractals, here, we present a new framework to define intrinsic notions of dimension on networks, the relative, local and global dimension. We showcase our method on various physical systems.
Bridging brain and cognition: A multilayer network analysis of brain structural covariance and general intelligence in a developmental sample of struggling learners
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.
A functional network analysis of the mouse brain: Insights into sexual imprinting
FENS Forum 2024