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We are looking for one highly motivated Bioinformatician/Data scientist with expertise in bioinformatics, machine and deep learning related areas to join our interdisciplinary group, as independent researcher. As part of our technological team, we expect the candidate to help us discover the hidden information underlying complex data (clinical information, DNA-seq, RNA-seq, single-cell and histopathological medical image data), developing innovative multi-modal integrative and deep learning-based models able to translate research findings into personalized healthcare strategies. The common purpose leading the research activities is the progression toward a data-driven precision medicine with a main focus on rare hematological diseases.
Job Responsibility:
Processing and analysis of clinical, -omics and imaging data
harmonization of complex and highly fragmented data
Investigate, define and support the implementation of scalable computational models in order to extract relevant features for improving personalized medicine programs
Collaborate in research and development of innovative techniques for understanding disease-specific patterns from multi-modal and heterogeneous data
Explore, define and support the clinical validation of the statistical and AI/ML integrative models applied to real-world data
Contribute to solutions design and establishment of requirements
Visualize data, report effective results and derive useful knowledge using a data-driven approach
Self and team-management on individual and team-sized studies’ deadlines
Collaborate with international partners on cross-academic research projects
Requirements:
Master Degree’s or PhD (preferably but not mandatory) in Bioinformatics or Computational Biology, Biomedical Engineering or Computer Science or STEM related disciplines
Understanding of –omics data structures and modeling
Experience in -omics data analysis is preferably
Experience in developing algorithms for data integration investigating disease-specific relevant markers, exploring dimensionality reduction methods to identify latent patterns and key features for clinical process improvement
Good knowledge of machine learning and deep learning techniques (e.g. k-NN, SVM, Random Forests, CNN, autoencoders, etc.) applied to healthcare
Good knowledge of statistical methods applied to medical data
Good scripting and programming skills (R, Python, bash)
Working knowledge of containers technologies (Docker and/or Singularity)
Fluent in written and spoken English and Italian
Nice to have:
Experience in cloud (GCP, AWS) and/or distributed computing (HPC) is appreciated
Experience in pipeline development, of reproducible research (e.g. git) and/or reproducible software development is a plus