Post-doc in Systems Pharmacology augmented by AI
il y a 3 jours
We are looking for a
post-doc researcher
in SYSTEMS PHARMACOLOGY AUGMENTED BY AI at Limoges University, France, commencing as soon as possible. This is a two-year position funded through a public-private partnership. Salary is dependent on working experience. In addition, this position is intended as a pathway to a junior professorship of four years, leading to the possibility of a permanent professor position.
There is no deadline for applications
,
and interviews will be organized based on suitability to the job.
We are seeking outstanding candidates with a PhD and expertise in causal AI and/or pharmacometrics, who are eager to advance research at the interface of multiscale and multisource modeling—integrating AI with mechanistic approaches—particularly in the domain of personalized therapy with anti-infective and immunosuppressive drugs. The successful candidate will join the dynamic research team « Pharmacology and Transplantation, » working alongside pharmacometricians, data scientists, biostatisticians, and other researchers. The team has authorized access to nationwide healthcare databases, ensuring immediate opportunities for high-impact research.
Limoges offers an excellent quality of life, with affordable living costs and a rich cultural and natural environment in the heart of France.
Research project
The selected candidate must be able to adapt and implement recent algorithms to predict clinical outcomes such as treatment efficacy and adverse effects, disease progression, and/or patient survival. The candidate must monitor and adapt recent algorithms in the fields of causal machine learning, symbolic regression, and data augmentation. Alongside ML/DL approaches, numerous published PK, PK/PD, disease progression, or system pharmacology models will serve as an important data source. They cover multiple scales from molecular data with PB/PK and PK/PD studies, plasma/tissue PK models, to individual-level exposure/response models or adverse effects models.
These models will be used to generate data via Monte Carlo simulation, which can be combined with anonymized experimental data using deep learning methods. These multi-source and multi-scale data sets will allow the creation of meta-models through ML/DL approaches (enabling the management of non-linear relationships between levels and the combination of various types of data). These models may involve creating latent variables to summarize relationships between different variables. Clustering methods will be used to
detect specific at-risk subgroups. Predictive performance of efficacy (graft survival for transplantation or bacterial eradication for anti-infective drugs) or occurrence of adverse effects will be evaluated in external databases.
Main Responsibilities:
Develop and integrate multi-scale models using ML and DL approaches.
Co-supervise doctoral research, including study planning and resource management.
Maintain constant scientific and technological watch to stay at the forefront of progress in pharmacometrics and predictive algorithms.
Publish research results in at least three Q1-Q2 scientific journals during the two years of funding.
Collaborate with clinical and industrial partners to clinically validate and valorize developed models.
Personal Qualities:
Strong motivation and passion for research and innovation.
Ability to work as part of a team and collaborate with various stakeholders.
Ability to write scientific articles.
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