Statistical modeling and estimation of molecule diffusion in fluorescence microscopy Level of qualifications required: PhD or equivalent
Function: Temporary Research Position
Level of experience: From 5 to 12 years
Context Understanding molecular interactions and intracellular transport mechanisms in space and time is central to fundamental cell biology to characterize cellular functions, and crucial to the treatment and cure of human disease.
In that context, it is of primary interest for biologists to visualize and estimate molecular mobility within the cell.
To address this issue, we have developed a novel Eulerian method inspired by previous works that allows estimating the diffusion and drift parameters attached to moving biomolecules within cells from 2D/3D individual trajectories.
The method estimates both drift and diffusion in local neighborhoods centered on trajectory points.
The local spatiotemporal kernel estimators correspond to weighted averages of the trajectory elements.
Unlike existing methods, the estimation of two or three-dimensional drift vector and the diffusion coefficient are performed on trajectory-sliding kernels, calculated at coordinates corresponding exactly to the coordinates given by the preliminary particle tracking.
Each particle track point is labeled into three motion categories: confined motion (subdiffusion), Brownian motion (free diffusion), and directed motion (superdiffusion).
The method is currently tested in several biological studies such as dynamics of transcription factors in the nucleus and MReB proteins in bacterial walls.
Assignment The objective of the 10-month project is to improve the machine learning-based classification of trajectories by estimating the Hurst exponent in space and time, as well as to provide a user-friendly Python software able to adapt to multiple scenarios in cell imaging and for a large range of applications.
This software will be embedded within the BioImageIT middleware designed for end-users and biology labs.
Main activities Develop a method and an algorithm to estimate the Hurst exponent, in space and time, for trajectory classification. Evaluate the method on artificial and real datasets. Ensure interoperability with the BioImageIT platform. Present the advancements to collaborators. Skills Skills in statistics, machine learning, and stochastic processes in biophysics Skills in image and microscopy data analysis Benefits package Partial reimbursement of public transport costs 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 (after 6 months of employment) and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.
) Social, cultural, and sports events and activities Warning: You must enter your e-mail address in order to save your application to Inria.
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Processing of applications sent from other channels is not guaranteed.
Instruction to apply Defence Security:
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No.
2011-1425 relating to the protection of national scientific and technical potential (PPST).
Authorization to enter an area is granted by the director of the unit, following a favorable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST.
An unfavorable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy:
As part of its diversity policy, all Inria positions are accessible to people with disabilities.
About Inria Inria is the French national research institute dedicated to digital science and technology.
It employs 2,600 people.
Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines.
The Institute also employs numerous talents in over forty different professions.
900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.
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