About this role
<p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">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. </span></span></span></p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">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. </span></span></span></p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">The AI Center of Humanitas is focused in research in the field of Artificial Intelligence applied in healthcare. Research and development areas include predictive and decision-support systems based on data-driven model (ML/DL models) to optimize clinical processes and ultimately improve the quality of patient care. We are a team of multidisciplinary scientists who work day by day on e-health and AI projects, by collaborating with clinical staff (doctors, nurses, researchers) and management staff.</span></span></span></p> <p> </p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif"><b>Responsibilities and Main Activities</b></span></span></span></p> <ul> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Processing and analysis of clinical, -omics and imaging data; harmonization of complex and highly fragmented data;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Investigate, define and support the implementation of scalable computational models in order to extract relevant features for improving personalized medicine programs; </span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Collaborate in research and development of innovative techniques for understanding disease-specific patterns from multi-modal and heterogeneous data; </span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Explore, define and support the clinical validation of the statistical and AI/ML integrative models applied to real-world data;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Contribute to solutions design and establishment of requirements;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Visualize data, report effective results and derive useful knowledge using a data-driven approach;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Self and team-management on individual and team-sized studies’ deadlines;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Collaborate with international partners on cross-academic research projects.</span></span></span></li> </ul> <p> </p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif"><b>Qualifications and required skills </b></span></span></span></p> <ul style="margin-bottom:11.0px"> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">We invite applications from highly motivated and outstanding students with a Master Degree’s or PhD (preferably but not mandatory) in one of the following disciplines: Bioinformatics or Computational Biology, Biomedical Engineering or Computer Science or STEM related disciplines. </span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Understanding of –omics data structures and modeling. Experience in -omics data analysis is preferably;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">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;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Good knowledge of machine learning and deep learning techniques (e.g. k-NN, SVM, Random Forests, CNN, autoencoders, etc.) applied to healthcare;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Good knowledge of statistical methods applied to medical data; </span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Good scripting and programming skills (R, Python, bash);</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Working knowledge of containers technologies (Docker and/or Singularity);</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Experience in cloud (GCP, AWS) and/or distributed computing (HPC) is appreciated;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Experience in pipeline development, of reproducible research (e.g. git) and/or reproducible software development is a plus;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Fluent in written and spoken English and Italian;</span></span></span></li> </ul> <p> </p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif"><b>Soft Skills</b></span></span></span></p> <ul style="margin-bottom:11.0px"> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Excellent team-working capabilities even with colleagues from different research areas and backgrounds;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Strong self-motivation, commitment and proactive approach;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Ability to meet deadlines and work autonomously in rapidly changing environments;</span></span></span></li> <li><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Curiosity and ability of stepping outside your comfort zone.</span></span></span></li> </ul> <p> </p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif"><b>Why you should consider this opportunity</b></span></span></span></p> <p><span style="font-size:11.0pt"><span><span style="font-family:Calibri, sans-serif">Humanitas Research Hospital is investing in data driven research and development of clinical support tools based on AI, you will contribute to the design and application of breakthrough technologies to be deployed in advanced clinical institutions. </span></span></span></p> <p><span style="font-size:11.0pt"><span><span style="font-family:'Calibri', sans-serif">You will get access to an extraordinary group of talented and passionate people coming from fields ranging from clinical sciences and healthcare management to informatics, bioinformatics and systems biology, including our extended international research network.</span></span></span></p> <p> </p> <p> </p> <p><span style="font-size:11.0px"><em>All candidate data collected from the application shall be processed in accordance with applicable law: Dlgs 198/2006 e dei Dlgs 215/2003 e 216/2003; privacy ex artt. 13 e 14 del Reg. UE 2016/679.</em></span></p>