Dear colleagues,

We are proposing a two-year post-doctoral fellowship, co-supervised by
AgroParisTech (Palaiseau, France) and CBGP in Montpellier, to develop
inference methods for the joint analysis of population dynamics and
population genetics data, in order to estimate the local and recent
demographic functioning of populations.

We are looking for a motivated researcher, with a PhD in population
genetics, population dynamics, probabilities or statistics applied
to genetics.

The project is detailed below.

Sincerely,

Camille Coron, Sophie Donnet, Raphaël Leblois, Miguel De Navascues

Two-year post-doctoral position at AgroParisTech - INRAE (France)

We are offering a 2-year postdoctoral position aimed at understanding
how combining demographic and genetic data could improve the inference
of a population's demographic history. We are looking for a motivated
researcher, with a PhD in population genetics or in probabilities or
statistics applied to genetics.

Localisation : AgroParisTech - INRAE (Palaiseau, France)
Duration : 2 years
Beginning : September 2025 at the latest

Subject : To study the demographic dynamics of a population, two types
of data and approaches are generally used: on one hand, count data, and
on the other hand, data from genetic sequenc- ing. These two types of
data require different mathematical techniques to infer the demographic
parameters of the populations under consideration.

The aim of this postdoctoral position is to propose probabilistic models
to jointly represent count data, coming from capture-mark-recapture
protocoles or from citizen science programs, and genetic sequencing
data. The properties of these models will be studied and these models will
be used to infer the demographic dynamics of the considered population.
These probabilistic models could range from the Wright-Fisher
model to more complex birth-and-death models with interaction and
spatial structure. Different modes of genetic transmission will also
be considered.  Some of these models could be studied using the SLiM
program, which allows for the simulation of individual-based models and
associated genetics.

The associated biological challenges are the study of a population's
demographic dynamics, and more specifically, understanding the decline
and displacement of certain populations due to climate warming, as well
as the control of proliferating species.

The mathematical approaches envisaged are (1) the joint probabilistic
modeling of population dynamics and datasets from different protocols,
containing count data on one side and genetic data on the other. These
models may have a natural hierarchical structure and must integrate the
different acquisition protocols for the different types of data. (2)
The inference of these models parameters, by combining inference by
simulation and by maximum likelihood estimation.  Finally, these models
could be confronted to real data, for example, concerning the monitoring
of fishery stock dynamics or the control of pest insect populations
(i.e., disease vectors, crop pests), in collaboration with the CBGP
(Montpellier).

Supervisors :
- Camille CORON (INRAE, Mathématiques et Informatiques Appliquées de
  Paris-Saclay)
- Sophie DONNET (INRAE, Mathématiques et Informatiques Appliquées de
  Paris-Saclay)
- Raphael LEBLOIS (INRAE, Centre de Biologie pour la Gestion des
  Populations, Montpellier)
- Miguel DE NAVASCUES (INRAE, Centre de Biologie pour la Gestion des
  Populations, Montpellier)

Searched profile : PhD in theoretical population genetics or in
probability or statistics applied to population genetics. A strong
interest for modeling and applications is required.

For more informations and to apply : camille.coron@inrae.fr,
raphael.leblois@inrae.fr, miguel.navascues@inrae.fr

Send a resume, a motivation letter, and at least one recommendation
letter.

Linked references :
[1] Giraud et al. 2016 Capitalizing on opportunistic data for monitoring
    relative abundances of species. Biometrics 72:649-658
[2] Benjamin C Haller, Philipp W Messer, SLiM 3: Forward Genetic
    Simulations Beyond the 1 Wright-Fisher Model, Molecular Biology
    and Evolution, Volume 36, Issue 3, March 2019, Pages 632-637
[3] Navascu´es et al. 2009 Characterization of historical demographic
    expansions from pairwise comparisons of haplotypes using linked
    microsatellites. Genetics 181:1013-1019
[4] Raynal et al. 2019 ABC random forests for Bayesian parameter
    inference. Bioinformatics 35:1720-1728
[5] Rousset et al. 2024 Better confidence intervals in simulation-based
    inference. BioRxiv
[6] Stoehr & Robin 2024 Composite likelihood inference for the Poisson
    log-normal model. ArXiv

raphael.leblois@inrae.fr

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