One-year Postdoctoral Position in Machine Learning (possible extension)

RESEARCHER PROFILE: Postdoc / R2: PhD holders 
RESEARCH FIELD(S)1: Computer science; Engineering; Mathematics; Medical science
MAIN SUB RESEARCH FIELD OR DISCIPLINES1: Machine Learning

JOB /OFFER DESCRIPTION 
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. 

Main duties: 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.

Activities: 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 to the valorisation of cancer databases, particularly the population-based ones. In this context, a key indicator is net survival that represents the survival that would be observed in a hypothetical world where people died only from the studied disease. Taking into account mortality due to other causes, derived from general population life tables stratified on certain variables, it enables comparisons between populations and trends to be studied. However, using insufficiently 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 life tables. However, the models are based on 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 Python. 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).

TYPE OF CONTRACT: TEMPORARY / JOB STATUS: FULL TIME /HOURS PER WEEK: 37h30
APPLICATION DEADLINE: 15/06/2024 09:00
ENVISAGED STARTING DATE: 15/04/2024
ENVISAGED DURATION: 12 months (possibility of extension)
JOB NOT FUNDED THROUGH AN EU RESEARCH FRAMEWORK PROGRAMME

WORK LOCATION(S): SESSTIM Lab (Sciences économiques et sociales de la santé & traitement de l’information médicale,  Campus Timone, Faculté de Médecine , 27 Bd Jean Moulin , 13005 Marseille France

WHAT WE OFFER:

Remuneration: Postdoctoral level; Aix-Marseille Université salary scale.

Opportunities: 

• Collaboration: As a team member, the researcher will engage in collaborative efforts with peers both within and outside the team or country.
• International Exposure: The researcher will have the chance to present their work at international congresses.
• Teaching Involvement: The researcher will actively participate in the teaching units of the team. He/She will contribute to educational initiatives, including summer schools organized by the team.

Benefits: The SCASC (“Service Commun d'Action Sociale et Culturelle”) subsidizes meal costs and offers 50% transport fare coverage for work-related travel. It also provides sports courses, cultural activities, social support, and counselling services.

Additional information: The Euraxess Center of Aix-Marseille Université informs foreign visiting professors, researchers, postdoc and PhD candidates about the administrative steps to be undertaken prior to arrival at AMU and the various practical formalities to be completed once in France: visas and entry requirements, insurance, help finding accommodation, support in opening a bank account, etc. More information on AMU EURAXESS Portal 

QUALIFICATIONS, REQUIRED RESEARCH FIELDS, REQUIRED EDUCATION LEVEL, PROFESSIONAL SKILLS, OTHER RESEARCH REQUIREMENTS (years of research experience (max. 3000 characters)

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.

Language skills

• English: scientific level (reading, writing, speaking).

Diploma level and experience

• PhD / Postdoctoral position with a specialization in biostatistics, data sciences, mathematics or applied statistics.

Soft skills: 

• Autonomy, excellent organizational skills and thoroughness;
• Ability to work in a dynamic environment and meet deadlines;
• Capacity to listen, analyze and summarize;
• Capacity to work with multidisciplinary teams;
• Being a source of proposals.

REQUESTED DOCUMENTS OF APPLICATION, ELIGIBILITY CRITERIA, SELECTION PROCESS
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

The candidate should have a doctoral degree in science with a strong academic record and peer-reviewed publications. 

Candidates will be shortlisted based on their research proposals and publications, followed by a rigorous interview process. Interviews will be conducted by visioconference or face-to-face in Marseille.

HOW TO APPLY/ Send to roch.giorgi@univ-amu.fr and nathalie.graffeo@univ-amu.fr

Website: https://sesstim.univ-amu.fr/fr