CRI recruits a postdoc at the Physics Inspired Machine Learning Machine
12+12 months French contract (CDD), full time. Available early 2020.
The Physics Inspired Machine learning team is looking for a Postdoc in the fields of scientific machine learning or computational physics.
The CRI Physics Inspired Machine learning team aims to develop to develop quantitative tools to extract human interpretable models from quantitative biological data sets. The work will combine the predictive power of neural networks with the interpretability of symbolic regression to develop a framework of interpretable AI and discover mechanistic models from biological data sets.
The job holder will be part of the CRI research team and will develop these deep learning based model discovery models and apply them to various biological data sets. As test case the aim is to apply our technologies to a commonly performed yet poorly quantified biological protocol: the disk diffusion assay for antimicrobial susceptibility testing (AST). The candidate will plan, develop the model discovery algorithms and potentially execute the experiments and results. The candidate is expected to help in supervising undergraduate interns that might enrol in the same project.
This job is for you if:
- You want to work an an innovative interdisciplinary team around life, learning and
- digital sciences,
- You have a PhD in physics, machine learning or related area,
- You are fluent in at least one programming language (preferably python)
- You have a solid background in the basic machine learning techniques (Pytorch, Tensorflow),
- You are eager to learn new techniques,
- You enjoy working in a dynamic environment
- You are willing to join a small but ambitious team of young researchers
The Center for Research and Interdisciplinarity (CRI) experiments and spreads new ways of learning, teaching, conducting research and mobilizing collective intelligence in life, learning and digital sciences. CRI Research is run as a Paris Descartes University Hosting Lab. CRI educational programs are accredited by the Sorbonne Paris Cité, Paris Descartes and Paris Diderot Universities). The Bettencourt Schueller Foundation has been an essential and key supporting partner since the CRI was created. A wide range of partners accompany the CRI including European Union H2020 funds, “Les Programmes d’Investissements d’Avenir” and Paris City Council who provided us with the building in the heart of Paris.
CRI is an atypical place, home to a community of students, educators, researchers, and engineers who may not fit into classical academic institutions because of their commitment to interdisciplinary, open science as well as innovative, out-of-the-box thinking. CRI helps catalyze the ideas, talent, and research actions, amplifying and channeling this contagious passion towards exponential change agents. We wish to face the unknown, at frontiers of disciplines and using state-of-the art facilities to construct frugal and scalable solutions for societal issues of today and tomorrow.
- Develop the deep learning based model tools
- Develop methods to infer antibiotic dose-response functions using deep learning methods
- Help in training and supervising undergraduate interns
- Possibility to work on an independent research project
Expected experience and competences
- Experience in machine learning
- Experience in programming (preferentially in Python)
- Proficiency in basic physics and biology
- Education requirements
- PhD completed
- English (full professional competence)
The position is based at the Center for Research and Interdisciplinarity (CRI) in the center of historical Paris (rue Charles V, 75004 Paris).
Postdoc income is commensurate to post-PhD experience, and follows the standard grid (Gross monthly income: around 2800-4300/month). Interested candidates should submit a formal application to firstname.lastname@example.org consisting of
(i) a current CV with past experience and programming skills highlighted (preferably with link to Github or equivalent),
(ii) a brief statement of research experience and interests (max. 2 pages) and
(iii) the contact information of up to two references (e-mail or phone number) with some context information (relationship to applicant).