The RADLAB at Northeastern University’s Network Science Institute has two postdoctoral positions available. We are looking for exceptional candidates who are interested in the following programs: 1. Trustworthy Network Science: As the use of machine learning in network science grows, so do the issues of stability, robustness, explainability, transparency, and fairness, to name a few. We address issues of trustworthy ML in network science. 2. Just Machine Learning: Machine learning systems are not islands. They are part of broader complex systems. To understand and mitigate the risks and harms of using machine learning, we remove our optimization blinders and study the broader complex systems in which machine learning systems operate.

The hired postdoctoral researcher will mainly work on WP2, i.e., on the development of new formalisms and methods to apply to higher order interaction patterns identified in the data analyzed in WP1. The project aims to build a theoretical and data analysis framework to demonstrate the role of higher-order interactions (HOIs) in human brain networks supporting causal learning. The Hinteract project includes three scientific work packages (WPs): WP1 focuses on developing an informational theoretical approach to infer task-related HOIs from neural time series and characterizing HOIs supporting causal learning using MEG and SEEG data. WP2 involves developing a network science formalism to analyze the structure and dynamics of functional HOIs patterns and characterizing the hierarchical organization of learning-related HOIs. WP3 is about compiling and sharing neuroinformatics tools developed in the project and making them interoperable with the EBRAINS infrastructure.

We’re hiring a post-doctoral researcher to join the Inverse Complexity Lab at IT:U, Linz, Austria. We are looking for an early-stage or more advanced postdoctoral scholar who is interested in building on our ongoing projects, or developing their own research agenda related to inverse problems in network science, complex systems modeling, and/or connections to machine learning. This position is not bound to a particular research project, and the successful applicant will enjoy intellectual independence and freedom to choose research topics. This position is guaranteed for 3 years. The gross salary range is € 66,532 to € 70,000 (corrected for inflation), depending on previous experience. The employment conditions in Austria include completely free health care (also for family members), social security benefits, 25 days per year of paid vacations, flexible working hours, and possibility of home office. In addition, IT:U will provide a KlimaTicket—a unified transport pass which gives free access to the entire transportation system in Austria, including trains and local public transport.

The School of Applied Mathematics at Fundação Getulio Vargas (FGV EMAp) in Rio de Janeiro, Brazil, invites applications for one open-rank faculty position in Data Science to strengthen and complement our existing research activity in this area. We are looking for established researchers (associate/full professor) or outstanding young researchers (assistant professor) who have demonstrated research and teaching expertise in Data Science. We will prioritize applicants whose research focuses on natural language processing, computer vision, reinforcement learning, network science and data mining, but we also welcome applications from other fields in Data Science. The successful candidate is expected to develop an externally funded research programme, publish in high-impact venues, supervise research (postgraduate) students, teach at both undergraduate and graduate levels, and provide service to the department and institution. In general, teaching duties consist of two courses per year, one at Undergraduate and one at Graduate level. Peer-reviewed external funding is expected to be obtained and sustained. Industrial partnerships are also strongly encouraged.

Tissue folding is a ubiquitous shape change event during development whereby a cell sheet bends into a curved 3D structure. This mechanical process is remarkably robust, and the correct final form is almost always achieved despite internal fluctuations and external perturbations inherent in living systems. While many genetic and molecular strategies that lead to robust development have been established, much less is known about how mechanical patterns and movements are ensured at the population level. I will describe how quantitative imaging, physical modeling and concepts from network science can uncover collective interactions that govern tissue patterning and shape change. Actin and myosin are two important cytoskeletal proteins involved in the force generation and movement of cells. Both parts of this talk will be about the spontaneous organization of actomyosin networks and their role in collective tissue dynamics. First, I will present how out-of-plane curvature can trigger the global alignment of actin fibers and a novel transition from collective to individual cell migration in culture. I will then describe how tissue-scale cytoskeletal patterns can guide tissue folding in the early fruit fly embryo. I will show that actin and myosin organize into a network that spans a domain of the embryo that will fold. Redundancy in this supracellular network encodes the tissue’s intrinsic robustness to mechanical and molecular perturbations during folding.