Summary

The Michael E. DeBakey Department of Surgery is seeking a Research Associate to implement and maintain the ATLAS (Applied sTatistics and machine Learning for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background in data science and machine learning, encompassing the application of statistical methods, the creation of predictive models, and the development of machine learning algorithms. Proficiency in Python, MATLAB, and R, combined with experience in signal and image analysis, as well as familiarity with electronic health data, will be distinct advantages.

https://www.bcm.edu/departments/surgery/research/atlas

Job Duties

• Implement and maintain the ATLAS initiative.
• Develop machine learning algorithms and create predictive models.
• Plan, direct and conduct research experiments.
• Evaluate and analyze data.
• Develop research techniques and performs applications required for specific research projects.
• May conduct literature searches and summarize information in an appropriate format for a particular study.
• Document results of experiments and reports to principal investigators.
• May supervise, schedule and coordinate the activities of less experienced research staff.
• Perform other job related duties as assigned.

Minimum Qualifications

• Master's degree in Basic Science or a related field.  Experience in lieu of degree will not be accepted.
• Three years of relevant experience typically as a Research Assistant, Research Technician or Postdoctoral Fellow/Associate.

Department Specific Criteria

• Must demonstrate a robust background in data science and machine learning, encompassing the application of statistical methods, the creation of predictive models, and the development of machine learning algorithms.

Preferred Qualifications

• Proficiency in Python, MATLAB, and R, combined with experience in signal and image analysis, as well as familiarity with electronic health data, will be distinct advantages.