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Postdoc Position at University of Florence
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Postdoc Position at University of Florence
Descrizione del lavoro
The University of Florence offers a post-doctoral researcher position in Bayesian Methods for Clinical and Observational Studies. Starting October 1st, the successful candidate will work on innovative strategies to improve drug development processes through Bayesian adaptive designs. The position allows for flexible working arrangements, with occasional presence in Florence required.
Competenze
- Ph.D. required.
- Non-Italian affiliation for at least 24 months in the last 3 years.
Mansioni
- Develop Bayesian methods for clinical and observational studies.
- Create adaptive randomization designs integrated with data from non-concurrent trials.
Formazione
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 veronica.ballerini@unifi.it
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