Postdoctoral Position in Machine Learning

Postdoctoral Position in Machine Learning

Hosting body

This position is based at the research unit SESSTIM (Health Economic and Social Sciences and Medical Information Processing) at the Timone Faculty of Medical and Paramedical Sciences, Marseille, France. SESSTIM works to produce excellent, multidisciplinary and interdisciplinary research in social sciences and public health that can lead to changes in the various fields of predictive, personalised, pre-emptive and participatory medicine. SESSTIM researchers develop, or are associated with, research projects that attempt to provide answers to current challenges facing society and its populations, and contribute to methodological developments and advances. In terms of pathologies of interest, our research focuses mainly on cancer and infectious and communicable diseases. Our questions focus on individual,
population and contextual factors. Our work targets populations in France, or more broadly in the North, but also in the South, mainly in the Mediterranean basin and sub-Saharan Africa.

Main missions

The candidate will work in the multidisciplinary “Quantitative Methods and Medical Information Processing (QuanTIM)” team, comprising researchers in epidemiology and public health, statisticians, biostatisticians, computer scientists and data scientists. More specifically, he/she will be assigned to a project involving the application and development of Artificial Intelligence techniques to data from cancer registries. The aim of the work will be to develop or adapt a machine learning methodology in order to estimate excess mortality in the case of insufficiently stratified general population life tables.


As part of the MIRACLE project (Méthodologie et Intelligence aRtificielle pour lA recherche épidémiologique en CancéroLogiE sur bases de données), funded by the French Ligue contre le Cancer, the candidate will contribute, for the benefit of patients and to decision-making I public health, to the valorisation of cancer databases, particularly the population-based ones. In this context, a key indicator measured in the general population is net survival, which represents the survival that would be observed in a hypothetical world where people died only from the disease under study. Taking into account mortality due to other causes, derived from general population life tables (1-3) on certain variables, it enables comparisons to be made between populations and trends to be studied. However, using insufficiently stratified general population life tables leads to biased estimates of excess mortality. Different approaches have been considered and different models have been proposed to estimate excess cancer mortality for variables not directly observed in general population life tables (1-3). However, the existing models are based on certain assumptions that may be considered too strong given the needs and epidemiological questions. The candidate will familiarise him/herself with the various approaches and models already developed, and will then investigate the contribution of approaches based on machine learning. He/she will develop or adapt a methodology based on machine learning (k-means, random forests or others) to estimate excess mortality in the case of insufficiently stratified general population life tables. The methodology developed should be adaptable to the situation where the number of variables not directly observed in the general population life tables is not limited. The candidate will assess the performances of these different methods through simulation studies. He/she will attach particular importance to the interpretation of the methods, with a focus on the epidemiological interpretability of the results obtained. He/she will implement the whole in an R package, preferably, or in another language depending on what is most suitable for practical application. Together with the other project investigators, he/she will write the article(s) on this work with a view to publication in international peer-reviewed journals (methodological and/or applied journals).


  • Knowledge
    • Strong theoretical and applied knowledge of machine learning techniques;
    • Knowledge and skills in survival analysis;
    • Expertise in model interpretation (e.g. SHAP technique);
    • Proficiency in R and/or Python programming languages.
  • Know-how
    • Autonomy, excellent organisational skills and thoroughness;
    • Ability to work in a dynamic environment and meet deadlines;
    • Capacity to listen, analyse and summarise;
    • Capacity to work with multidisciplinary teams;
    • Being a source of proposals.
  • Language skills
    • English: scientific level (reading, writing, speaking).
  • Diploma level and experience
    • PhD / Postdoctoral position with a specialisation in biostatistics, data sciences, mathematics or applied statistics


Professional environment – Place of work

This work is carried out within the research unit SESSTIM, Health Economic and Social Sciences and Medical Information Processing, at the Timone Faculty of Medical and Paramedical Sciences in Marseille

Start date: As soon as possible, depending on administrative recruitment deadlines.
Duration: 12 months, with the possibility of extension.
Remuneration: Postdoctoral level; Aix-Marseille University salary scale.


Send to and your application file consisting of:

  • A covering letter explaining how the applicant feels he/she can contribute to the project;
  • A curriculum vitae;
  • The thesis defence report.
  • A letter (or letters) of recommendation would be an advantage.
  • Reference of the offer (to be indicated systematically): MIRACLE-MLLT-24

Application Deadline

April, 30th 2024.
Interviews will be conducted by visioconference or face-to-face in Marseille.


  1. Touraine C, et al. More accurate cancer-related excess mortality through correcting background mortality for extra variables. Stat Methods Med Res. 2020;29(1):122‑36.
  2. Mba RD, et al. Correcting inaccurate background mortality in excess hazard models through breakpoints. BMC Med Res Methodol. 2020;20(1):268.
  3. Rubio FJ, et al. On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables. Biostatistics. 2019;22(1):51‑67.
PostDoc position – Machine Learning and nanosats to probe the interior of Solar System bodies

PostDoc position – Machine Learning and nanosats to probe the interior of Solar System bodies

Duration: 2 years


The study of asteroids is fundamental both for our understanding of the formation of the Solar System, or the supply of water and origin of life on a planet, and for the prediction of Earth impacts. Knowledge of the structure of small bodies (from a few meters to several hundreds of km) is an important element. Indeed the mass, the density, and the internal structure of the small bodies are as many key factors to understand their formation, and diversity, and tracing the origins of planetary systems in general. Moreover, making the link between the internal structure of small bodies and their external shape is the next major challenge in the field.

Nanosatellites or cubesats offer a new opportunity to perform gravity field determinations that we are developing within the BIRDY project. These local relative techniques and measurements show an innovative aspect for this type of interplanetary missions. In particular, we want to study the radio-science and POD precise orbit determination technique, considering and exploiting all the possibilities offered by inter-satellites links (ISL) radio links between one or more nanosatellites. This, in order to derive the lower order of the gravitational potential, mass, bulk density, mass distribution, etc. Our study will develop the concept of radio/optical measurements through inter-satellite links, within the BIRDY project, in the event of a reconnaissance mission (planetary defence, fly-by) or an exploration mission (planetary science, rendez-vous).

Moreover, the next challenge we want to tackle is to relate the external morphology of small bodies – modelled as gravitational aggregates – to their internal structure. We will develop in particular machine-learning ANN/PINN methodologies for inverse problems in the determination of gravity fields and tomography, as well as the modelling of the internal structure of gravitational aggregates with SSDEM numerical methods.


Three research axis will be covered with this post-doc work:

  • Develop precise orbit determination process to probe the gravity field through ISL (radio/optical). Analyse a space mission to asteroid Aophis close encounter in 2029. Derive an optimisation analysis for the measurements and nanosatellites configuration, assess the need for complementary ground-based measurements. Validate the approach with our RF-test bench deployed at CENSUS.
  • Develop a gravity-and-tomography global inversion algorithm, using artificial neural networks, to derive the internal structure of small bodies. This will be done by combining complementary radio-science (gravity) and LFR radar (tomography) observations.
  • Model granular systems using SSDEM numerical simulations, to provide a link between the external morphology of a body with it’s modelled interior.

The work will be performed at Paris observatory within IMCCE and CENSUS space centre. The methodology will be applied to several targets of interest, and in particular to the HERA mission from ESA.

Izzo & Gomez 2022 Commun. Eng. 1, 48
Martin et al. 2002 CMDA 134, 46
Hestroffer et al. 2021 7thPDC, #149
Hestroffer et al. 2019, AARv 27, 6

Candidate profile

  • PhD in computer science, machine learning, mathematics, A&A, data scientist, or equivalent
  • Experience in neural networks and regression
  • Strong skills in programming and numerical computations
  • Excellent written and verbal communication in English

Training and skills required

  • Advanced skills in data analysis and the use of statistical software
  • Very good understanding of Machine Learning theory and techniques
  • Strong experience with machine learning methods (ANN, PINN, …) for regression and inverse problems
  • Knowledge of gravitational field representation, and precise orbit determination appreciated
  • Knowledge of SSDEM numerical computations for granular systems is a plus



Employment address

Paris observatory
77 av. Denfert-Rochereau
75014 Paris

Part-time postdoctoral research scientist position

Part-time postdoctoral research scientist position

VIFASOM @Hôtel-Dieu-Paris

Pandore-IA project-Paris

  • Type of contract: part-time (60%) fixed-term contract
  • Level of qualification: PhD pr equivalent
  • Function: postdoctoral research scientist
  • Level of experience: recently graduated
  • Start date: as soon as possible, before January 2024

The Pandore IA project

The objective of this project is to develop and validate a set of statistical learning methods and models
for predicting the physiological states of individuals performing intense physical activities under
specific conditions or environments, based on EEG, actigraphy, biological biomarkers, heart rate,
temperature, respiratory parameters…. It includes an experimental phase that will be carried out “in
the field” in an operational environment proposed by the VIFASOM team (Vigilance Fatigue and Sleep
Team in Hôtel-Dieu, and Bretigny), in relation with the French Army research institute IRBA) and
possibly with the French National sport institute (INSEP). To this end, a dedicated and sovereign
experimental environment consisting of a set of software tools, hardware and technical equipment
will be classified, stored, processed, exploited and data shared.

The two main domains of analysis would be:

  • Detection of sleepiness and sleep states in operational situations and prediction of a staff’s state of consciousness (attention, awareness of the environment, interaction with the group and the environment, sleep, sleep disorders like insomnia, etc.)
  • Prevention of hyperthermia with real-time prediction of exercise heat stroke and heat intolerance

This project aims to address the above issues by proposing a set of multidimensional AI methods
applied to physiological or other monitoring data.
The collaboration between the Université Paris Cité (VIFASOM) and SAAGIE (Rouen-France) company
will enable a team of researchers, engineers and medical and PhD doctors to participate in the design
and development of software modules and predictive models. Once developed, the team project will
deploy these prototype tools and test them in an ad-hoc technical environment in line with existing
research and expertise protocols.


The candidate will be involved in an interdisciplinary approach between neuroscience-ideally sleep and
vigilance-EEG, cognitive science, consciousness and computational approaches. The main missions will
be to program, run and analyze databases issues from research addressed by the teams involved in
the project on sleepiness- of athletes or subjects facing sleep restriction or operational environment.
The candidate needs to show high programming skills using Python, matlab, R or similar softwares.
Experience with human research on sleep, vigilance and thermoregulation is needed, as well as
redactional skills to write scientific papers and advancement reports

  • Languages: Fluent English (speaking & writing)
  • Relational skills: good interpersonal relation skills
  • Other valued appreciated: sense of responsibility, self-driven, being curious, being team-playery

A regular basis physical presence will be expected for the term of the project. Working partly at home
may be discussed.

The supervising team

The medical and scientific supervision is organized with complementary skills for this interdisciplinary
project that associates human research data science and artificial intelligence with the supervision of
Damien Léger
MD, PhD, VIFASOM, Hôtel-Dieu, Paris, Mounir Chennaoui, PhD VIFASOM IRBA, Paul
Bouchequet VIFASOM and Fabien SAUVET MD PhD, IRBA VIFASOM. Romain Picot-Clemente and
Patrick Giroux, from SAAGIE will provide scientific supervision for computational modelling,
development of software modules and predictive model

The work environment

The research team will be based at the VIFASOM lab of the Hôtel-Dieu, a beautiful hospital in the heart
of ancient neighborhoods of Paris, where participants will come for testing and treatment.
@Hotel-Dieu is also an exciting and innovative digital health data center supported by APHP, Université
Paris Cité and a group of health start up. The VIFASOM lab currently houses 3 PI and > 10 PhD students
and engineers working on various fields of cognitive science. This will offer the opportunity for fruitful
discussions and collaborations with SAAGIE in Rouen and the scientists in a stimulating workplace.
Nearby, the Université de Paris, Paris Public Health, PRAIRIE datascience and the Ecole Normale
Supérieure also offer many opportunities for exciting scientific training and conferences in cognitive science. The PhD students will have courses and scientific supervision at the Doctorate School Bio SPC
of Paris.
All supervisors endorse values of equity and diversity, and are committed to ensuring a safe,
welcoming, and inclusive workplace. Everyone is therefore strongly encouraged to apply.


CV, motivational and recommandation letters should be sent to Pr Damien LEGER: Applications are reviewed on a rolling basis and all candidates will receive full consideration.


Université Paris Cité with its 61,000 students, 7,500 staff and 142 laboratories, is today the first
medical university in Europe and the University whose scientific publications are the most cited in
The research team EA 7330 VIFASOM (Vigilance Fatigue and Sleep) was created in 2013. Co-directed
by Professor Damien LEGER and Mounir CHENNAOUI, it includes about thirty researchers who work on various themes related to sleep (Epidemiology, Effects of deprivation on alertness, performance,
risk of injury, and more recently on machine learning and IA).

Saagie, a company created in 2013, provides a platform to facilitate and accelerate the development
and deployment of projects dedicated to the exploitation and exploitation of big data (Big Data). This
solution integrates the best Big Data technologies of the moment around a Plug & Play orchestrator
supporting the DataOps methodology and allowing to embed artificial intelligence bricks to respond
to multiple use cases in fields of applications diverse and varied. Saagie now has a multidisciplinary
team of 90 people in Rouen, Paris, New York and London.

One Tenure Track and Two postdoc positions (computational pathology ; machine learning and computational biology)

One Tenure Track and Two postdoc positions (computational pathology ; machine learning and computational biology)

Tenure Track position in AI for the life sciences

The CBIO is currently looking for a tenure track researcher with experience in Machine Learning / Computer Vision for biology and medicine. Click here for details (pdf)

Postdoc position at the CBIO in the field of Computational Pathology

The CBIO is currently looking for a postdoc in Computer Vision in the field of histopathology. Click here for more details (pdf)

Postdoc position in machine learning and computational biology

The CBIO is currently looking for a postdoc (up to 36 months) in machine learning and computational biology to work on integrating variability and epivariability at transposable elements to genotype-to-phenotype studies. Click here for details (pdf)