Postdoctoral Research Associate - Computational biology/computational mass spectrometry

Ludwig Princeton Branch, Princeton University; Princeton (NJ); On-site; USD 70,000 - 90,000; Full time

Job summary

Princeton University invites applications for a postdoctoral fellow or more senior researcher to work on computational analysis of mass spectrometry datasets, focusing on AI/ML methods to discover unknown metabolites linked to phenotypes.

The successful candidate will develop and apply computational approaches to metabolomics data, validate predictions with experimentalists, and contribute to metabolite discovery campaigns related to human disease, genetics, and diet. In-person on campus.

Qualifications

• PhD with strong publication record in relevant fields.
• Experience with computational mass spectrometry is valuable.

Responsibilities

• Lead computational analyses of mass spectrometry-based metabolomics datasets.
• Develop and apply AI/ML tools to assign structures to unidentified peaks.
• Collaborate with experimentalists to validate predictions and drive wet-lab discoveries.
• Mentorjunior researchers and contribute to career development.

Skills

Computational biology

Bioinformatics

Metabolomics

Machine learning

Mass spectrometry

Cheminformatics

Written communication

Independent work

Education

PhD in CS/Computational biology/Chemistry/Biochemistry

Tools

Mass spectrometry software

Job description

Princeton University: Office of the Dean of the Faculty: Natural Sciences: Ludwig Princeton Branch Description

The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational methods to discover unknown metabolites that have strong associations to experimental variables or phenotypes, but which have eluded identification. The positionwill remain open until excellent fits are found.

The successful candidate will develop and apply computational approaches to mass spectrometry-based metabolomics datasets, with discovery of unknown metabolites being a major focus. The laboratory is engaged in several metabolite discovery campaigns involving mining of large-scale metabolomic datasets to discover novel metabolites associated with human disease, genetics, and dietary exposures. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https://www.nature.com/articles/s42256-021-00407-x), and extending these tools or developing new models as needed. The candidate will have the opportunity to work directly with experimentalists to validate predictions and drive wet-lab discoveries, or to perform experiments themselves if they wish.

This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve goals in the next stage of their career.

The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments.The work location for this position is in-person on campus at Princeton University.

This position is subject to Princeton University's background check policy.

Qualifications

The successful candidate will be motivated, independent, and have strong written communication skills. Candidates are required to have experience in one or more of the following areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, metabolism/metabolomics, analytical chemistry/mass spectrometry, cheminformatics, or machine learning/computer science. Prior experience in computational mass spectrometry will be viewed favorably but is not a requirement if the candidate has complementary expertise.

Individuals should have or be expected to have a PhD with appropriate research experience in computer science, computational biology, biological or chemical engineering, chemistry, biochemistry, or a related field.Qualified candidates who pass an initial screening may be provided with short programming exercises to assess their skills.

Equal Employment Opportunity Statement

Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.

Pay Transparency Disclosure

The University considers factors such as (but not limited to) the scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

The University also offers a comprehensive benefits program to eligible employees. Please see link for more information.