JOB DESCRIPTION

The AI Researcher has experience in machine learning and deep learning applied to structured and unstructured medical data.
The candidate will research and develop new statistical and machine-learning methods for the analysis of medical and clinical records, applying Artificial Intelligence (AI) techniques on real-world healthcare data.
The candidate will contribute to developing new technologies for data synthesis and digital twins using a wide variety of machine learning and deep learning methods; investigating various research topics in machine learning and statistics to determine the best method for medical data synthesis and effective approach for generated data validation.

SKILLS AND QUALIFICATIONS

• Experience in developing machine learning and deep learning techniques and algorithms (such as k-NN, Naive Bayes, Support Vector Machines, Random Forests, etc) in healthcare, also applied to time-series/longitudinal data;
• Experience in developing generative models (e.g. statistical, GAN, VAE, etc.) applied to medical data for synthetic data generation and digital twins;
• Good knowledge of Computer Vision and/or NLP is appreciated;
• Experience in applied statistics skills, such as distributions, statistical testing, regression, etc;
• Good scripting and programming skills;
• Good proficiency in Python, R programming languages;
• Experience with data science frameworks (e.g. tensorflow, pytorch, scikit learn, scipy, pandas, numpy) and visualization frameworks (e.g. plotly, seaborn, matplotlib);
• Experience with cloud (GCP, AWS, Azure) and/or distributed computing is appreciated;
• Knowledge of MLOps practices, IT infrastructures, back end frontend development is appreciated;
• Master (PhD would be a plus) in a STEM discipline;
• Fluent in written and spoken English and Italian;

SOFT SKILLS

• Excellent team-working capabilities even with colleagues from different research areas and backgrounds;
• Strong self-motivation, commitment and proactive approach;
• Ability to meet deadlines and work autonomously in rapidly changing environments;
• Curiosity and ability of stepping outside your comfort zone.

• Collaborate in research and development of innovative generative data models for effective synthetic data generation and digital twins in healthcare;
• Development of statistical, machine learning and deep learning models on medical data, including time-series/longitudinal data;
• Explore, define and support the clinical validation of the statistical and machine learning models applied to real-world data;
• Exploratory data analysis and integration of highly fragmented data;
• Visualize data, report effective results and derive useful knowledge using a data-driven approach;
• Collaborate with international partners in both private industry and academia;