We are seeking a highly motivated and qualified candidate for a full-time Associate Research Scientist (ARS) position in the Division of Molecular Therapeutics. The ARS will be in charge of carrying out computational and practical aspects of projects focused on adhesion G protein-coupled receptor (aGPCR) biology using advanced microscopy methods. The qualified candidate will work closely with division members including undergraduate and graduate students, postdoctoral and senior research scientists, and faculty members.

• Apply advanced data analysis and machine learning techniques to extract meaningful insights from large-scale microscopy datasets.
• Develop and implement computational algorithms and models for image analysis, feature extraction, and quantitative measurements.
• Collaborate with cross-functional teams, including biologists, chemists, and computer scientists, to understand project requirements and define analytical approaches.
• Design and optimize workflows for preprocessing, cleaning, and transforming microscopy data to ensure high-quality analysis and interpretation.
• Conduct statistical analysis and hypothesis testing to identify patterns, correlations, and significant findings within the data.
• Communicate complex technical concepts and analytical results to non-technical stakeholders through clear visualizations, reports, and presentations.
• Stay up-to-date with the latest advancements in microscopy techniques, image analysis methods, and data science tools relevant to pharmaceutical research.
• Participate in the development and implementation of data management strategies, including data storage, integration, and security.

• Responsible for developing and performing experiments and analysis strategies alongside other members of the lab, preparing results for presentations, writing manuscripts for publishing, and contributing to broader aspects of collaborative projects in the lab.
• Communicate with and mentor other lab members in the development and deployment of such assays in addition to fostering any necessary collaborations.

Minimum Qualifications:

• PhD or equivalent degree in neuroscience, psychiatry, biophysics, systems biology, or another related field with a computational orientation.
• Fully proficient in object-oriented programming within MATLAB and Python, and standard machine-learning modules implemented within these environments.
• One or more publications in peer-reviewed journals evidencing competency with machine learning or other advanced data analysis methods.

Preferred Qualifications:

• PhD or equivalent degree in neuroscience, psychiatry, biophysics, systems biology, or another related field with a computational orientation with >3 years postdoctoral experience.
• Proficiency in sterile mammalian cell culture techniques and a strong familiarity with high-content imaging and immunocytochemistry methods.
• Previous experience in the development and optimization of biophysical sensors for GPCR activity, demonstrated through practical application and proficiency in molecular cloning techniques.
• Comfort with storing and analyzing large datasets, for example through the use of SQL, TensorFlow, PyTorch, HDF5 file formats.
• Proven track record of implementing machine learning approaches such as support vector machines, neural networks, or Bayesian classifiers.
• Demonstrated ability to work effectively across experimental and computational groups, and demonstrable previous collaborative success is essential.

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

About Columbia University

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