3 Apr 2024
Job Information
- Organisation/Company
Inria- Research Field
Computer science- Researcher Profile
Recognised Researcher (R2)- Country
France- Application Deadline
2 May 2024 - 00:00 (UTC)- Type of Contract
To be defined- Job Status
Full-time- Hours Per Week
To be defined- Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme- Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
Contexte et atouts du poste
This PhD project will be realized in the Inria NERV team, a research lab supported by the French institutions Inria, Inserm, CNRS, and Sorbonne University. The team is located in the Paris Brain Institute (ICM) within the Pitie-Salpetriere hospital.
The NERV team pursues a multidsciplinary research program at the intersection between biomedical engineering, complex systems and clinical neuroscience. NERV proposes new computational frameworks to analyze and model the spatiotemporal complexity of brain networks from multimodal and longitudinal neuroimaging data, and we design noninvasive intervention strategies based on brain-computer interfaces. Furthermore, the team ejoys a privileged position within a unique scientific and technological environment including comprehensive experimental core facilities (eg, neuroimaging, genetics, cellular), several animal models (eg, from nematodes to humans) and powerful centralized cluster computer system to realize big-data analysis and simulations.
Mission confiée
Context of the project
Artificial Intelligence (AI) and especially Deep Learning (DL) have undergone many successes in recent years in various domains of applications such as computer vision, speech recognition, language, domain recognition, decision-making, even outperforming the human capacities benchmark in most of them.
Those performances were mainly obtained by increasing scales : data augmentation and bigger models launched on GPUs and faster learning units. However many features of human ability described by cognitive sciences seem to remain completely out of reach for now. The main one being the generalizability beyond past experience, namely the adaptability to unknown contexts. Furthermore, deep learning algorithms always require a huge amount of data while adult brains can learn new tasks with a very few examples. So the question is how real brains came up with such e cient versatility and what are the associated organizational features?
Recent developments in network science have provided fresh insights into the structure and dynamics of the brain organization from a system perspective [ 1 , 2 ]. By modeling brains as graphs, with nodes accounting for brain regions and edges for anatomical/functional connections between them, a better understading of the organizational properties of the nervous system became possible [ 3 ]. Experimental evidence across disparate temporal and spatial scales indicated that brain networks tend to exhibit key topological features such as node centrality, modularity and efficiency. Notably, network modularity is a fundemental mesoscale property characterized by the presence of functionally specialized, yet interdependent modules, and o ers several advantages such as functional factorization, adaptability to new tasks, and robustness against perturbations [ 4 , 5 ]. Furthermore, brain network modularity is correlated to difference of performance across individuals [ 6 , 7 ] and plays an important role in combining information from differently specialized modules to perform more complex tasks. In artificial networks, recent studies demonstrated that modular architectures could lead to improved performance in learning different compositional tasks [8, 9 ]. Thus, a crucial question is to understand why, where, and when mesoscale properties such as modularity emerge during the learning process [ 10 ].
Principales activités
Objectives
The main goal of the PhD project is to elucidate the role of mesoscales network structures in generalizable artificial intelligence. Speci cally, this project aims to:
+ Conceive analytical network models that lead to the emergence of signi cant mesoscale attributes, such as modularity, by integrating developmental insights. Provide a foundational understanding of the necessary conditions (eg, network size, topology, density) for such emergent properties.
+ Compare the results with those obtained from the brain wiring formation of di erent species (eg, nematode, humans). Finetune the model parameters based on the above mentioned biologically data and derive a neurophysiologically plausible interpretation.
+Develop a novel training framework that takes into account the model architecture, the learning algorithm and the multimodal nature of real inputs. Evaluate the overall performance when confronted with unfamiliar scenarios, thereby evaluating their versatilty and robustness.
Main Activities
+ Theoretical modeling . The initial phase of this doctoral research involves the development of
analytical models to understand the emergence and stability of significant mesoscale properties, such
as modularity, within biological networks during developmental processes. It is posited that modularity
manifests as a consistent outcome in neural networks influenced by a variety of parameters throughout
the development of organisms. This investigation aims to elucidate the prerequisites for such emergent
modularity across different species. Furthermore, the research will explore potential phase transitions
towards modular networks in response to variations in these parameters.
+ Convergence with biological data . In a second step we will test and fit those models on biological
data over several species on the whole lifespan from the embryonic stage of development to the
adult age. We will rst study small species for which the whole brain networks (i.e. the connectomes)
are known. We will compare the mesoscale properties obtained in the synthetically-generated network
models and those in the actual connectomes. Connectomes needed to experimentally validate network
models are already available in the framework of different past and current research projects granted
to the PIs team.
+ Development of new artificial neural architectures . The last phase of this research project
will focus on leveraging biological insights to guide the design of artificial neural architectures, aiming
to foster the emergence of highly effcient network properties such as functional specialization, since
they have been shown as unable to achieve it [ 9 ]. Finally we also propose to explore how local
learning algorithms for energy-based models could play a role in artificial networks mesoscale properties
emergence such as modularity [ 11 ].
Compétences
Required skills
The ideal candidate should have a solid background in experimental physics, machine learning and data analysis, as well as experience in laboratory projects and simulations (Python, MATLAB). The ability and willingness to learn will do equally well.
Avantages
- Subsidized meals
- Partial reimbursement of public transport costs (75%)
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking
- Flexible organization of working hours (after 12 months)
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
Requirements
Additional Information
Work Location(s)
- Number of offers available
- 1
- Company/Institute
- Inria
- Country
- France
- Geofield
Where to apply
- Website
https://illbeback.ai/job/phd-position-f-m-campagne-doctorant-2024-emergence-of-…
STATUS: EXPIRED
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