Job Offer: Engineer in Probabilistic Machine Learning for Building Workflows to Operate DIGITAL TWIN Brain Models in EBRAINS
Summary:
The Theoretical Neuroscience Group (TNG), under the direction of Viktor Jirsa, is pleased to announce an opportunity for an engineer to join our team. This position is at the intersection of probabilistic AI/ML and the flexible integration of tools within the EBRAINS framework. EBRAINS AISBL is a prominent contributor to the European digital neuroscience research infrastructure (https://www.ebrains.eu/). Our exciting research program centers around DIGITAL TWIN Brain Models, leveraging neuroimaging data and the power of probabilistic AI/ML to drive inference services. We aim to enhance scalability and reliability, facilitating the translation of personalized predictive medicine into clinical applications and innovations. Our goal is to seamlessly integrate this workflow into the EBRAINS ecosystem.
As an engineer, the candidate will play a pivotal role in the implementation and development of generative models, including methods such as Normalizing Flows and Variational Autoencoders. These models are designed to construct flexible inference workflows for brain network models, an integral part of The Virtual Brain framework (http://www.thevirtualbrain.org). We apply these models to various brain imaging modalities, such as EEG, SEEG, MEG, and fMRI. To maximize the predictive power of personalized brain models, we will integrate and develop automatic tools that not only make predictions but also quantify associated uncertainties in adaptive or maladaptive responses, covering aspects like degeneration, dedifferentiation, and degeneracy.
Required Qualifications:
PhD or Master degree in engineering, computer science, physics, statistics, computational neuroscience, or a related discipline.
Experience in one or more of the project research topics, which include generative models (Normalizing Flows/VAEs), or Simulation-based inference (preferably with related peer-reviewed publications).
Proficiency in programming with languages such as Python, Julia, or C++ and expertise in scientific computing packages (JAX) and infrastructure.
Desired Qualifications:
Familiarity with Jax/Pytorch/Tensorflow.
Experience running parallelized large-scale simulations on supercomputers.
Expertise in data science, data visualization, openMINDS metadata schemes, BIDS standard.
Terms of Salary and Employment: (Salary and benefits details, if any, would be included here.)
The initial appointment is for one year, with the potential for extension up to three years.
Application Deadline: The position will remain open until filled.
Starting Date: January 2023
How to Apply: Interested candidates are encouraged to submit their applications, which should include a cover letter, curriculum vitae, and contact information for references. Please send your application to giovanna.RAMOS-QUEDA@univ-amu.fr and lisa.otten@univ-amu.fr, and be sure to specify "Postdoc Application - Machine Learning for Brain Network Models" in the subject line.
We are enthusiastic about the opportunity to welcome a highly motivated and qualified postdoctoral researcher to our team. Your contributions will play a vital role in advancing our understanding of large-scale brain network models within the context of neuroscience and psychiatric disorder research. We look forward to the collaboration and innovation you will bring to our team.
UMR 1106 – Institut de Neurosciences des Systèmes INS Faculté de Médecine de la Timone
27 Bd Jean Moulin - 13385 Marseille cedex 05. France
Tel : + 33 (0) 4 91 32 42 21 ou 23 | Fax : + 33 (0) 4 91 78 99 14