Training and Clinical Education Director jobs in Lowell, MA

We are a highly collaborative group at the Center of Biomedical and Health Research in Data Sciences (CHORDS) at University of Massachusetts, Lowell seeking PhD or Postdoc candidates to start in 2024.

Our research focuses on using data science and applied machine learning to understand the biology of common diseases like cancer and dementia. These diseases occur as the result of many influences accumulated over a lifetime, and our goal is to discover new ways to prevent or treat them through mining health records and genomic data.

We are seeking Phd or Postdoc trainees for projects including (but not limited to!):

• Mining health records data to learn hidden beneficial effects of common medications on disease outcomes
• Modeling knowledge graphs to discover new drug interactions
• Integrating genetic and health history to uncover new disease subtypes influencing clinical outcomes.
• Learning the biology of unexpected drug effects by integrating genomic and clinical data

Trainees will work with some of the best cutting edge data sources including large health records data with over 20 million participants; biobank data sources, and genetics and genomics data.

The candidate will be mentored by Dr. Rachel Melamed, an expert in biomedical data science, and co-mentored by Dr. Tingjian Ge, a computer science professor and expert in graph neural network models. We also have collaborators with expertise in neurodegenerative disease, epidemiology, and biostatistics. PhD programs include computer science, applied biology, or biomedical engineering, depending on the background and interests of the candidate.

Our lab has a focus on mentorship and maximizing the potential for each trainee to achieve their career goals in a healthy, motivated, supported environment. Trainees are invited to attend national and international conferences in the field. Lowell is in greater Boston, and a short drive from the seashore and mountains.

Trainees should already have computational experience including experience in applying machine learning models, though not necessarily in the biomedical context.