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 Assistant Researcher will be a key player in advancing our capacity in bioinformatics and biomedical data science.The successful candidate will perform scientific activities and be responsible for:Develop an innovative research program in the areas of Health Data Science and Bioinformatics, such as Transcriptomics, Multiomics, 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 biological or 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 the broader scientific community by participating and disseminating results at international conferences in the areas of Health Data Science and Bioinformatics.Publish 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.Apply for and acquire competitive research funding. Scientific profile:The ideal candidate will possess:PhD in disciplines such as Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or related areas.A strong foundation in Health Data Science and Bioinformatics, having demonstrated a solid expertise in areas such as Machine Learning and Computational Genomics. 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.Proven track record of research productivity and publication in reputable journals. 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 FMUL.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.