Research Support Specialist: Cell Biology & Lab Operations

The Research Foundation for The State University of New York at Stony Brook; Stony Brook (NY); On-site; USD 42,000 - 64,000; Full time

Job summary

The Research Foundation for The State University of New York at Stony Brook is seeking a Research Support Specialist to assist with routine laboratory research and daily operations in the Lab of Dr. Min-Guk Cho in the Department of Pharmacological Sciences and Cancer Center.

The role emphasizes independence and collaboration, with opportunities to learn new methods and contribute to experimental optimization.

Qualifications

• Bachelor’s degree and bench research experience in biology; related field.
• Experience with mammalian cell culture or basic cell/molecular biology techniques.

Responsibilities

• Perform or assist with routine cell and molecular biology work, including mammalian cell culture, sample processing, preparation of media, buffers and reagents, and established experimental procedures.
• Maintain laboratory supplies, samples, freezer inventories, research records and basic equipment.
• Assist with ordering supplies and equipment and maintain related inventory and purchasing records.
• Support general laboratory organization, shared work areas, routine equipment maintenance, and established laboratory procedures.
• Participate in laboratory meetings and safety/compliance activities; after training, may assist with limited animal-related procedures.
• Other duties as assigned.

Skills

Bench research

Molecular biology

Cell culture

Data interpretation

Education

Bachelor's degree in Biology or related field

Tools

Laboratory equipment handling

Job description

The Research Foundation for The State University of New York at Stony Brook is seeking a Research Support Specialist to assist with routine laboratory research and daily operations in the Lab of Dr. Min-Guk Cho in the Department of Pharmacological Sciences and Cancer Center.

The role emphasizes independence and collaboration, with opportunities to learn new methods and contribute to experimental optimization.