- Collaborate in research and development of innovative generative data models for effective synthetic data generation and digital twins in healthcare;
- Contribute to the design, development and deployment of medical software, code repositories and end-to-end pipelines using appropriate MLOps tools;
- Design and deploy machine learning pipelines in a production environment;
- 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;
- Innovative algorithms design and data analysis to discover and assess significant dynamics and patterns from complex data,
- Collaborate with international partners in both private industry and academia;
- Knowledge of machine learning/deep learning models and statistical techniques;
- Understanding of data structures, data modeling and software architecture;
- Experience in developing machine learning and deep learning solutions (classification, regression, clustering and generative models) in healthcare;
- Experience in designing and building structured software and pipelines for technical users, such as Data Scientists;
- Good proficiency in Python and R programming languages;
- Experience in using frameworks such as Keras, PyTorch and Tensorflow;
- Experience with deployment including knowledge of CI/CD, GIT versioning, containerization (Docker), MLOps practices and related concepts;
- Knowledge of cloud providers (GCP, AWS, Azure) and/or distributed computing;
- Knowledge of database systems and data lakes, good knowledge of SQL is appreciated;
- Ability to design and develop backend API services and simple web pages for data science web applications;
- Experience with Computer Vision, NLP;
- Good knowledge of generative AI and LLM is preferred;
- Master (PhD would be a plus) in a STEM discipline;
- Fluent in written and spoken English and Italian;
- 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.
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Machine Learning Engineer AI Center - Rozzano, Italia - Humanitas
Descrizione
The Machine Learning Engineer has experience in machine learning and deep learning applied to structured and unstructured medical data.
Your main contribution will be focused on developing models to answer clinical questions with evidence in a R&D setting using data from the electronic health record. You will routinely interface with clinical stakeholders to evaluate KPI, update the modelling strategy and course-correct unverified hypothesis. You will be developing libraries to share with your team and leverage already existing ones.
The ML Engineer will develop and deploy AI solutions while also improving already existing pipelines.
The candidate will also contribute to developing new technologies for data synthesis and digital twins using a wide variety of machine learning and deep learning methods.
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.
Responsibilities and Main activities
Skills and Qualifications
Soft Skills
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.
