11 programming-language "Data driven Materials Modeling" positions at Human Technopole
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supported by the Human Technopole Ciliopathies Flagship Research Programme , a multidisciplinary initiative that brings together several Research Groups to dissect the molecular mechanisms underlying ciliary
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management of the project, ensuring its delivery in line with the scientific objectives, the approved work plan and the obligations of the Grant Agreement and the Consortium Agreement, allowing the Scientific
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or technical contributions through publications, open-source software, models, or comparable research outputs. Excellent programming and software engineering skills in Python, with experience building scalable
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fundamental questions in biology using state-of-the-art technologies and large-scale datasets. Within this mission, the Biostatistics Unit of the Centre for Genomics – Population and Medical Genomics Programme
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of molecular phenotypes, Mendelian Randomization, co-localisation); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and
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procedures, sets up recruitment commissions for scientific and technical staff, defines the Foundation's internal regulations, proposes amendments to the Statute, and submits the economic plan and financial
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. Experience in software development, research software engineering, machine learning engineering, data science, or similar roles. Strong programming skills in Python. Familiarity with scientific or machine
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initiatives. How to apply Submit: • CV • Motivation letter (English) • Contact details of two referees This is a permanent contract. Salary: up to € 105.000,00, Managerial Level. The position is based in Milan
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states, tissue organization, and biological programs, and connect these representations to mechanisms of human disease. Your work will sit at the intersection of machine learning, computational biology
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identify the cells, cellular states, and biological programs through which genetic variation influences disease. Your work will sit at the intersection of statistical genetics, machine learning, and