Research Scientist in Computational Metabolomics

2 weeks ago


Princeton, New Jersey, United States Penn Foster Inc Full time
Position Overview:

Penn Foster Inc is seeking to fill a position for a Research Scientist specializing in computational metabolomics. This role will focus on the application of advanced machine learning techniques to analyze mass spectrometry data.

Key Responsibilities:
  • Develop and implement artificial intelligence and machine learning methodologies to identify both known and novel metabolites from mass spectrometry datasets.
  • Lead innovative projects, such as converting tandem mass spectrometry data into chemical structures for the de novo annotation of unknown metabolites.
  • Design machine learning workflows to enhance the preprocessing of liquid chromatography-mass spectrometry (LC-MS) experiments.
  • Collaborate closely with experimental teams to integrate computational analyses with experimental datasets.
  • Engage in data curation and preprocessing to address challenges posed by low-quality or noisy data.
Research Opportunities:

The successful candidate will have the chance to contribute to a variety of exciting research initiatives, including:

  • Integrating language models with mass spectrometry data.
  • Developing bioinformatics and cheminformatics tools for the discovery of natural products.
  • Conducting large-scale meta-analyses of mass spectrometric datasets.
Qualifications:

Candidates should possess a PhD in computational biology, chemistry, biochemistry, computer science, biological engineering, or a related discipline, along with a proven track record in one or more of the following areas:

  • Computational biology or bioinformatics
  • Cheminformatics
  • Analytical chemistry, particularly in mass spectrometry or metabolomics
  • Machine learning or computer science

Strong written communication skills and a motivated, independent work ethic are essential. Candidates should have demonstrated their expertise through at least one first-author publication in relevant fields.

Application Process:

Interested candidates are encouraged to submit their CV and a cover letter that highlights 1-3 relevant publications or preprints. Additionally, please provide contact information for three references. Candidates who pass an initial screening may be asked to complete programming exercises to evaluate their skills. Only qualified candidates will be contacted.



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