Research Assistant: Data, Lab & ML Analytics

Elevate Clinical Research; Houston (TX); On-site; USD 45,000 - 66,000; Full time

Job summary

Elevate Clinical Research is seeking a Research Assistant to advance healthcare and social assistance research by supporting data-driven studies and experimental protocols in Houston, TX. This role involves collecting, processing, and analyzing complex datasets to inform clinical practices and health policies.

The successful candidate will work with interdisciplinary teams to design methodologies, including computational and laboratory techniques, and contribute to machine learning model

Qualifications

• Proficiency in data collection methods and laboratory techniques including gel electrophoresis.
• Experience with programming and data analysis tools such as TensorFlow, NumPy, and SPSS.
• Basic understanding of algorithms, neural networks, and deep learning concepts.

Responsibilities

• Conduct comprehensive data collection through field research and laboratory experiments, ensuring accuracy and reliability.
• Utilize software tools such as TensorFlow, NumPy, and SPSS to preprocess, analyze, and visualize research data.
• Develop and implement algorithms and deep learning models to analyze complex biological datasets.
• Perform gel electrophoresis and other laboratory techniques to support experimental research objectives.
• Collaborate with research team members to design experiments, interpret results, and prepare reports and publications.

Skills

Data collection

Lab techniques

Machine learning concepts

Data analysis

Organization

Education

Master’s degree in health data analytics or computational biology

Tools

TensorFlow

NumPy

SPSS

Job description

Elevate Clinical Research is seeking a Research Assistant to advance healthcare and social assistance research by supporting data-driven studies and experimental protocols in Houston, TX. This role involves collecting, processing, and analyzing complex datasets to inform clinical practices and health policies.

The successful candidate will work with interdisciplinary teams to design methodologies, including computational and laboratory techniques, and contribute to machine learning model