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Assistant Professor/Computational Scientist

Computational Scientist

  • Syracuse
  • Physician, Faculty & Librarians
  • Full-time
  • Opening on: Jul 13 2023
  • Pathology
  • State of New York
  • Assistant Professor HS, NSC5
  • 77078
  • UUP (State University Professional Services Unit)
Job Summary:

We are seeking an expert in image analysis, machine learning, and computer vision to join our department as a Machine Learning (ML) Scientist. As part of an interdisciplinary team, ML scientist will work on developing advanced image analysis techniques (segmentation, classification, object detection, quantification) to support precision medicine applications. The successful candidate will work collaboratively with a diverse team of researchers, pathologists, and clinicians; develop best-in-class algorithms that directly address important biological and clinical questions, and incorporate image-based data with clinical and molecular data to drive translational and clinical research projects including in the field of immuno-oncology.

Machine Learning Scientist will do the following:

  • Develop multi-modal tools to improve the diagnostic practice of medicine and the efficacy of treatment of diseases such as cancer.
  • Participate in creating the modern pathology lab with new digital technologies
  • Leverage AI to identify novel image-based biomarkers
  • Collaborate with industry leading companies to solve challenging diagnostic problems
  • Scholarly work such as publications and presenting at conferences
  • Assist departmental pathology faculties to conduct the research projects
  • Train and supervise graduate and postgraduate trainees
  • Conduct exploratory AI experiments
Minimum Qualifications:

Qualifications:

  • Advanced degree (Ph.D. or M.D./Ph.D.) in computer science, biomedical engineering, biomedical imaging or a related field
  • Postdoctoral training and/or minimum 2 years of prior work experience

Required expertise:

  • Experience analyzing different modalities such as histopathology, radiology, spatial transcriptomics, single cell gene expression, or bulk sequencing data using state-of-the-art ML techniques
  • Experience working with various open sources databases such as TCGA, GEO, HubMAP and others
  • Experience developing, training, and evaluating classical machine/deep learning models, such as SVMs, Random Forests, decision trees, image segmentation (U-NET), object detection autoencoders, Gradient Boosting, CNN, FCN, ResNet, GAN, clustering, PCA and etc.
  • Experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, C++, TensorFlow, and Keras)
  • Experience with large-scale imaging data and formats (e.g., pathology images), and image analysis software (Visiopharm, Halo, QuPath, Image J or others)
  • Experience with the following tools and Python libraries: OpenCV, Pandas, NumPy, Pillow, Matplotlib, GDAL, LFlow, GitLab, SageMekr, AWS, MySQL, Azure DevOps
Preferred Qualifications:

 

Work Days:

Monday - Friday 7:30am-4:30pm

Salary Range/Pay Rate:

DOQ           

Message to Applicants: Please submit CV and cover letter with application.  Rank dependent upon qualifications. Recruitment  Office: Stacy Mehlek, Faculty Affairs & Faculty Development Executive Order: Pursuant to Executive Order 161, no State entity, as defined by the Executive Order, is permitted to ask, or mandate, in any form, that an applicant for employment provide his or her current compensation, or any prior compensation history, until such time as the applicant is extended a conditional offer of employment with compensation. If such information has been requested from you before such time, please contact the Governor's Office of Employee Relations at (518) 474-6988 or via email at info@goer.ny.gov.              

 

We are an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status or disability or other protected classes under State and Federal law.

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