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Application Deadline
31
Dec 2019
2019-12-31 8:00:00 2019-12-31 18:00:00 Europe/Paris Internship: Bayesian approaches to Biomolecules Random Walks with mixed properties Description   The TramWay project aims at designing an entirely automated procedure to study the random walks (RWs) of biomolecules in living cells. At the nanoscale, the dynamics of individual biomolecules is inherently random, […] "25-28 rue du Dr. Roux, 75015, Paris" Jean-Baptiste Masson jean-baptiste.masson@pasteur.fr

Description

 

The TramWay project aims at designing an entirely automated procedure to study the random walks (RWs) of biomolecules in living cells. At the nanoscale, the dynamics of individual biomolecules is inherently random, governed by thermal noise and stochastic molecular interactions. By giving access to the full distribution of molecular properties, rather than simply their average value, the great advantage of single molecule measurements is thus their ability to identify static and dynamic heterogeneities, and rare behaviours. Local heterogeneities, numerous specific and non-specific interactions, and unknown number of molecular partners in the cellular environment lead to extremely unusual properties of biological RWs, making the inference of their nature a challenging statistical and computational problem.

 

More information on the application of TRamWAy to synapses is available at the following address: https://research.pasteur.fr/en/project/mapping-receptor-dynamics-in-synapses/

 

We are proposing 2 internships for graduate students and 1 internship for an undergraduate student

 

Internship 1

We are proposing an internship of up to 6 months for a motivated applicant. She/he is expected to contribute to our mixed temporal/spatial analysis of RW. She/ he will combine Bayesian time series analysis and point process modelling to infer temporal and spatial processes from single molecule recordings in 2D/3D at the full cell scale. She/he will also develop a variational inference scheme to simultaneously infer multiple dynamical maps from single datasets. We are looking for either a physicist with a strong statistical physics background or a computer scientist with experience in complex, large-scale variational inferences.

 

Internship 2

We are proposing an internship of up to 6 months for a motivated applicant. She/he is expected to contribute to our effort to characterize unusual RWs that cannot be described by canonical model, e.g. such as a fractional Brownian motion or a hidden Markov model. She/he will develop Bayesian non-parametric approaches to characterize and classify the temporal and spatial characteristics of these unusual RW. We are looking for either a physicist with a strong statistical physics background or a computer scientist with experience in complex, large-scale variational inferences.

 

Internship 3

We are offering an internship for a motivated undergraduate applicant. She/he is expected to contribute to our effort to analyze biomolecules RW at the big data scale. She/he will develop pipelines to entirely automate single molecule analysis. The pipelines will leverage TRamWAy and will also perform post analyses in order to automatically generate reports providing interpretation of the results. We are looking for a physicist with a background in statistical physics or a computer scientist with experience in inferences.

 

 

The successful candidates will have privileged access to multiple GPU clusters in the Pasteur institute as well as a CPU cluster. Furthermore, she/he will join a full team working on the project composed of Christian L. Vestergaard, who developed a non-tracking algorithm using belief propagation and unseen graph summation to infer biomolecule dynamics at high density, Francois Laurent, who designed the TRamWAy software platform and is currently developing stochastic gradient approaches to infer time varying 2D/3D maps of biomolecule dynamics at the full cell scale, and Alexander Serov, who after designing Bayesian evidence tests for Ito-Stratonovitch dilemma is developing amortized approaches to detect out-of-equilibrium dynamics from recorded RWs.

 

Required Skills

For a physicist:  statistical physics and random walk theory

For a computer scientist: Bayesian Inference, structured and variational inferences

Scripting language experience: Bash, Python

 

Duration

Up to 6 months

 

Localization

Institut Pasteur (Paris)

 

Contact Information

Interested candidates should contact Jean-Baptiste Masson (jbmasson@pasteur.fr) with their CV and a brief motivation letter.