Bayesian adaptive phase I-II trials for drug resistant infection

Contract type : Fixed-term contract

Renewable contract : Yes

Level of qualifications required : PhD or equivalent

Fonction : Temporary scientific engineer

Context

Within the framework of the PEPR SN SMATCH.

This research project focuses on integrating mechanistic mathematical models, that link the dose regimen to the concentration and/or the efficacy markers, into the design model and the dose escalation process of a Phase I/II clinical trial. Clustering techniques will be added to the code aiming at early identification of responding patients. A Bayesian framework will be used and decisions will be made on predicted probability of successes for each enrolled patient.

Assignment

Collaborations:

The recruited person will be in connection with the whole SMATCH consortium (https://pepr-santenum.fr/2023/11/07/smatch/ ).

Responsibilities :

Mission 1: Looking for innovative methods to improve existing models. 
Mission 2: Translating models into codes and software. 

Main activities

Main activities :

  • Bibliography search of existing approaches;
  • Improving the coding and explanations of dose-finding methods;
  • Testing and adjiusting the code;
  • Participating in scientific articles writing;
  • Participating to conferences.

Skills

Technical skills and level required:

  • Advance knowled in mathematics (pure or applied)
  • R and/or Phyton coding

Languages:

  • Fluent in English

Relational skills:

  • Feel confortable to work in a team

Benefits package

  • Subsidized meals
  • 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 and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage