Postdoctoral fellow (Research Associate) in Federated Learning and Analysis for Health Research | Luxembourg City, Luxembourg | University of Luxembourg

Position:	Postdoctoral fellow (Research Associate) in Federated Learning and Analysis for Health Research
Institution:	University of Luxembourg
Location:	Luxembourg City, Luxembourg
Duties:	As a Postdoctoral Researcher, you will develop and implement federated analytical workflows tailored for health research. You will apply AI/ML algorithms to analyse a diverse range of data types, including clinical, molecular (-omics), and (sensor/mobile and PROMs/PREMs) within a federated environment. Additionally, you will innovate state-of-the-art federated AI/ML methods, to ensure privacy and data security in clinical research. To augment federated analysis, you will be generating synthetic data using ML techniques, such as Generative Adversarial Networks (GANs). Your workflows and methods will be incorporated by a multidisciplinary team into the CLINNOVA platform for federated data management and analysis. You will take an active role on project activities and effectively disseminating findings to the project members and the scientific community through project meeting, conferences and publications
Requirements:	A PhD in computer science, information technology, computational biology, bioinformatics, or a related field, with keen interest in health research and related IT infrastructure; Domain knowledge: Good understanding of statistical analysis principles and AI/ML techniques in both centralized and federated environments; Hands-on experience in developing, deploying, and maintaining ML operations (MLOps) within IT infrastructure, including familiarity with virtualization and containerization technologies such as Docker and Kubernetes is considered advantageous

Text:	Postdoctoral fellow (Research Associate) in Federated Learning and Analysis for Health Research As a Postdoctoral Researcher, you will develop and implement federated analytical workflows tailored for health research. You will apply AI/ML algorithms to analyse a diverse range of data types, including clinical, molecular (-omics), and (sensor/mobile and PROMs/PREMs) within a federated environment. Additionally, you will innovate state-of-the-art federated AI/ML methods, to ensure privacy and data security in clinical research. To augment federated analysis, you will be generating synthetic data using ML techniques, such as Generative Adversarial Networks (GANs). Your workflows and methods will be incorporated by a multidisciplinary team into the CLINNOVA platform for federated data management and analysis. You will take an active role on project activities and effectively disseminating findings to the project members and the scientific community through project meeting, conferences and publications A PhD in computer science, information technology, computational biology, bioinformatics, or a related field, with keen interest in health research and related IT infrastructure; Domain knowledge: Good understanding of statistical analysis principles and AI/ML techniques in both centralized and federated environments; Hands-on experience in developing, deploying, and maintaining ML operations (MLOps) within IT infrastructure, including familiarity with virtualization and containerization technologies such as Docker and Kubernetes is considered advantageous