Postdoctoral Fellow: ML for Clinical Pain & Mindfulness Trial

Worcester Polytechnic Institute; Worcester (MA); In loco; USD 65.000 - 75.000; Tempo pieno

Descrizione del lavoro

Worcester Polytechnic Institute invites applications for a Post-Doctoral Fellow to apply machine learning to predict clinical pain outcomes and participate in an NIH-funded trial. The role offers mentorship and freedom to pursue new ideas, with exposure to clinical trial processes and collaboration with academic and clinical researchers.

The fellow will lead research activities, including multimodal data integration, development and validation of predictive models, and preparation of manuscripts

Competenze

• PhD in Computer Science or a related field.
• Strong knowledge of machine learning and programming skills.
• Proficient in English and able to interact with multidisciplinary teams.
• Experience applying ML to clinical medicine or healthcare is preferred.
• Interest in clinical trials and app development.

Mansioni

• Lead research activities, including multimodal data integration and pre-processing.
• Develop and validate predictive machine learning models.
• Communicate ML results to a multidisciplinary team of computational and clinical experts.
• Collaborate on interpretation of results in the context of clinical medicine.
• Prepare manuscripts for publication.

Conoscenze

Machine learning

Programming skills

English communication

Research in healthcare

Clinical trials interest

App development

Formazione

PhD in Computer Science or related field

Strumenti

Python

Descrizione del lavoro

Worcester Polytechnic Institute invites applications for a Post-Doctoral Fellow to apply machine learning to predict clinical pain outcomes and participate in an NIH-funded trial. The role offers mentorship and freedom to pursue new ideas, with exposure to clinical trial processes and collaboration with academic and clinical researchers.

The fellow will lead research activities, including multimodal data integration, development and validation of predictive models, and preparation of manuscripts