Postdoc Position at University of Florence

The International Society for Bayesian Analysis; Ancona; Ibrido; EUR 30.000 - 40.000; Tempo pieno

Descrizione del lavoro

A prestigious university offers a post-doctoral researcher position focused on Bayesian Methods for Clinical and Observational Studies. The role, located in Florence with flexible working arrangements, involves innovative approaches to drug development and analysis of diverse patient populations. Candidates need a Ph.D. and relevant international experience. The position starts on October 1st with a duration of 16 months.

Competenze

• Required Ph.D. in relevant field.
• Non-Italian affiliation for at least 24 months in last 3 years.

Mansioni

• Develop Bayesian methods for heterogeneous populations.
• Enhance drug development processes.
• Analyze studies concerning intercurrent events.

Conoscenze

Bayesian Methods

Clinical Trials

Causal Inference

Precision Medicine

Formazione

Ph.D.

Descrizione del lavoro

Postdoc Position at University of Florence

Aug 31, 2023

The Department of Statistics, Computer Science, Application of the University of Florence is offering a post-doctoral researcher position in Bayesian Methods for Clinical and Observational Studies, starting on October 1st.

Location : Department of Statistics, Computer Science, Application, University of Florence. Working from a different location allowed (occasional presence in Florence may be required)

Duration : 16 months

Deadline : September 10th

Interview : a few days after the application – to be agreed

Requirements : Ph.D. + a non-Italian affiliation for at least 24 months in the last 3 years.

Project title : Bayesian Methods for Clinical and Observational Studies

Abstract : Traditional development of new drugs and biomarkers has followed the “one drug, one target disease” strategy, which is costly and inefficient and often leads to failure. Contrary to this strategy, we aim to create Bayesian Methods for Clinical and Observational Studies that study heterogeneous populations, target several diseases, and test various treatments from the pre-clinical and clinical stages. The aim of the project is to speed up the drug development process while increasing the power of results and biomarker discovery using adaptive randomization. We will develop novel and ethical Bayesian adaptive designs that : integrate data coming from non-concurrent trials; change the randomization ratios using covariate-dependent models; provide a way to analyze the study in the presence of intercurrent events, i.e., events that take place after the randomization and may bias the study results, in a causal framework.

Keywords : adaptive trials, causal inference, precision medicine

Contact : Send a CV to

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