PRR Project

Summary

Job description:Improved biomedical data science capability is high in the agenda of key stakeholders, such as healthcare practitioners, government, industry, patient associations and academic research groups. However, these stakeholders are struggling to develop the technical and scientific expertise to remove the analytical capability bottleneck and unleash the potential for producing knowledge from data. This is because data access and analysis, while promising and invaluable, poses ethical, computational, and analytical challenges that are not currently within reach of most research groups.The Principal Researcher will be a key player in advancing FMUL academic mission and capacity in biomedical data science.The selected candidate is expected to:Develop an innovative research program in the areas of Bioinformatics and Health Data Science, such as Computational Biology, Transcriptomics and Multiomics (namely single-cell and spatial methods), Predictive Analytics, Medical Imaging Analysis, Genomics, Healthcare Management Analytics, Electronic Health Records Analysis, and Personalized Medicine. Lead research projects focused on computational analysis of complex molecular and health-related data sets, and collaborate with biomedical and clinical researchers to establish a strong foundation in data analysis at the host institution.Contribute to an increase the number of research applications to international research funding calls by promoting cross-departmental collaborations, methodological quality and analytical capability, access and use of preliminary data, accelerating the research potential of biomedical and clinical researchers.Foster better integration and use of data science resources across stakeholders, including researchers, pharmaceutical companies, scientific societies, patient associations, local and national government bodies to improve translational and outcomes research.Contribute to the broader scientific community by participating and disseminating results at international conferences in the areas of Health Data Science and BioinformaticsPublish research results in high-impact peer-reviewed scientific journals.Mentor and supervise MSc and PhD students, as well as postdoctoral researchers in the areas of Health Data Science and Bioinformatics at the host institution.Apply for and acquire competitive research funding. Scientific profile:The successful candidate will have a strong foundation in Bioinformatics and Health Data Science, having demonstrated a solid expertise in areas such as Computational Biology, Machine Learning, and Computational Genomics.The ideal candidate for this research position will possess: PhD in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or related areas. Strong experience with Python and/or R, machine learning libraries and AI/ML frameworks;Sound understanding of statistical, machine learning and deep learning algorithms.Strong experience with genome-wide analysis of next generation sequencing data (e.g. RNA-seq, Ribo-seq, ChIP-seq, CLIP-seq, ATAC-seq) at either bulk or single-cell level;Sound understanding of statistical and computational methods used for omics data;Experience visualizing and manipulating large data sets;Strong problem-solving, analytical, and critical-thinking skills;Excellent communication and collaboration skills.Demonstrated experience in mentoring and supervision of researchers in different career levels.Proven track record of research productivity and publication in reputable journals.Proven track record in research funding acquisition and leading large research projects Rationale for hiring:This scientific area is a strategic and effective key to release, capacitate and uplift the clinical/medical research potential, leading to more resourceful and valuable medical studies at the School of Medicine. This hiring will allow a transformative change at FMUL in the underdeveloped, but crucial, area of health data science by increasing research excellence and capacitating the areas of machine learning, biostatistics, and bioinformatics.