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Apply to the DPH PhD Cohort (31 January 2022)

What is the DPH PhD Cohort? The Digital Public Health Graduate Program offers to all the doctoral students from the University of Bordeaux, whose research project is directly related ... Cohort, PhD ... Apply to the DPH PhD Cohort

SAVE THE DATE! - Register from now on to our new PhD training "Introduction to the Bayesian Framework for Biometrics" (24 March 2021)

Dear PhD students from the DPH Cohort, ED SP2, ED MI, ED SVS you are all invited to register to this new PhD training offered by the Graduate Program Digital Public Health! /!\ Students from

Welcome Day 2022 (07 September 2022)

Last Monday 5th September we welcomed our new MSc Public Health Data Science cohort for this academic year 2022-2023. We hope you have a great experience at the Université de Bordeaux!

Call for Applications-Eiffel Scholarship (12 October 2022)

cohort, do not hesitate to apply !

International Mobility (22 November 2022)

and to participate in international trainings and/or conferences for all DPH PhD cohort students. The PhD student must: /!\ All PhD students from the DPH cohort MUST apply to the DPH international ... -term international mobility projects for all Digital Public Health PhD cohort students.

Call for PhD Applications 2021 (31 March 2021)

The Digital Public Health Graduate Program offers a PhD position to excellent local and international applicants, and a 6-12 months thesis extension grant to its PhD cohort through its own selection ... From March 29 to May 30 2021, the call for applications for the international PhD program in Digital Public Health is open!! /!\ But there is more...This year, PhD students from the DPH cohort

Save the date for the virtual DPH Coffee Break (27 November 2020)

to invite you to the DPH Coffee Break to present you the Graduate Program, introduce the DPH team and tell you how to become part of our DPH PhD cohort and its advantages. Due to the current sanitary context

Apply in PhD Funding (25 April 2022)

and high dimensional observations into mechanistic models Develop causal inference techniques for the complex dynamic settings encountered in epidemiological cohorts Improve individual predictions