Postdoctoral: Artificial Intelligence and Machine Learning Analyses 613 views

The Barman laboratory at Johns Hopkins University is looking for a postdoctoral fellow for the development and application of artificial intelligence and machine learning analyses to address a wide range of biological problems. Our lab works on the development of new technologies for the analysis of complex biological systems via advances in optics, nanomaterials, and artificial intelligence. We collect large Raman spectroscopy and label-free quantitative phase imaging datasets of cells, tissues, and biomolecules. Computational methods will be needed to annotate and analyze those datasets, as well as to relate function and structure, to ultimately help transform images into scientific discoveries. We invite you to join us in developing methods that have real-world applications and are necessary to further enhance our understanding of biology.

Applicant’s job responsibilities will include:

  • Build machine learning models in Python using popular machine learning libraries.
  • Build and train supervised machine learning models for prediction and binary classification tasks, including multiple linear regression, logistic regression, neural networks, and decision trees.
  • Unsupervised, weakly-supervised, and interactive learning.
  • Segmentation and classification of biological structures.
  • Tracking and event detection in 2D and 3D movies.
  • Processing of very large datasets.
  • Working with an interdisciplinary team and collaborating with experimentalists.

Required Education:

  • PhD. in Computer Science, Machine Learning, Statistics, Computational Biology, or a related field.
  • Advanced knowledge of Machine Learning, Statistics, Calculus, Data Structures, and Algorithms.

Skills and Abilities:

  • Strong background in computer vision, machine learning, and deep learning.
  • Experience with large optical microscopy datasets, deep-learning frameworks, Python, and MATLAB.
  • Enthusiasm for using computational approaches to learn to discover biological insights
  • Strong publication track record.
  • Excellent scientific writing and oral communication skills, as well as the ability to work effectively and collaboratively in a self-directed manner with others.
  • A high level of productivity for peer-reviewed publications is expected.

Additional information about the group, including a full list of publications and research areas, can be found here:

Application: Interested candidates should send a CV (including publications), a cover letter containing a brief description of their research experience and interests, and contact information for three individuals who can provide letters of reference upon request to:

To ensure full consideration, applications should be received by January 17, 2023. Applications submitted earlier than this date will be considered first. The expected start is as soon as possible after the closing date.

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