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Position Detail

The Spraggins laboratory in the Mass Spectrometry Research Center at Vanderbilt University is looking for recent Ph.D. graduates interested in developing machine learning and image analysis methods to enable integration, analysis, and mining of multi-omic imaging data including imaging mass spectrometry, highly multiplexed immunofluorescence microscopy, and spatial transcriptomics. Successful candidates will have a background in Image Processing, Statistical Analysis, and/or Machine Learning. We have funded opportunities available that focus on several biomedical research projects including (1) creating comprehensive molecular atlases of human organs, (2) kidney disease, (3) infectious disease, and (4) Alzheimer’s disease. Our research group leads a dynamically growing molecular imaging center, that is part of 3 major NIH consortia, and involves several laboratories and many collaborators from diverse fields in the US and Europe.

Cell and Developmental Biology

Our research group develops integrated molecular imaging technologies to elucidate the molecular basis of health and disease. Modern instrumentation and computing capabilities have enabled researchers to move beyond reductionist biology and, instead, probe how the components of biological entities (e.g. molecules, cells, and tissues) interact globally to reveal the underlying biology of disease. This systems biology approach has been accelerated by advancements in high-throughput ‘omics’ technologies, however, genetic and molecular information is only part of the story. The challenge lies in understanding how these parts interact and how perturbations to the system relate to disease. To address this challenge, we are advancing instrumental capabilities and developing the computational tools necessary to integrate and mine multimodal data sets that bring together imaging mass spectrometry, highly multiplexed immunofluorescence microscopy, and spatial transcriptomics.

The Spraggins laboratory is part of the Mass Spectrometry Research Center and Department of Cell & Developmental Biology at Vanderbilt University. We have multiple funded projects available to develop computational data integration, analysis, and mining tools for our multimodal analysis pipelines that leverage imaging mass spectrometry, highly multiplexed immunofluorescence microscopy, and spatial transcriptomics. These projects involve exciting biomedical research applications including constructing multimodal molecular atlases of (1) normal aging tissue as part of the NIH Human Biomolecular Atlas Project (HuBMAP,, (2) kidney disease as part of the NIDDK Kidney Precision Medicine Project (KPMP,, (3) molecular and cellular neighborhoods around pathological hallmarks of Alzheimer’s Disease, and (4) the interface between host and pathogen in infected tissues.

Successful candidates will focus on advancing the state-of-the-art in data integration across modalities to solve challenging problems in processing, evaluating, and interpreting molecular imaging data. Working closely with our instrumentation development teams and clinical collaborators, the candidate will be responsible for inventing, developing, and validating machine learning and computer vision algorithms. He/she will develop a set of semi-automated and automated image processing and analysis applications for image segmentation, classification, registration, feature extraction, and pattern detection. Post-doctoral candidates joining our team will have access to our state-of-the-art computational resources including a dedicated computation environment on a local server with CUDA-enabled GPUs as well as high-performance computing resources through Vanderbilt’s ACCRE and the Pittsburg Supercomputing Center. 

Our group and close collaborators are made up of a diverse team of researchers with backgrounds in bioinformatics, computer science, mathematical engineering, bioanalytical chemistry, cell biology, molecular biology, neurology, and pathology. In addition to the aforementioned computational resources, we have cutting-edge molecular imaging instrumentation including two Bruker timsTOF Flex platforms, a 9.4T and 15T Bruker Fourier transform ion cyclotron mass spectrometers with MALDI and ESI sources, a Bruker Rapiflex MALDI tissuetyper, a GeoMX Digitial Spatial Profiler and Illumina NGS system for spatial transcriptomics, a Zeiss Axio Observer Z1 inverted microscope integrated with an Akoya CODEX system, and a Zeiss Axio Scan Z1 brightfield and fluorescence slide scanner. 


• Independent problem solver: able to plan and execute development projects and experiments independently.
• Bioinformatics and/or computer science background. Focus on image analysis is a plus.
• Proficiency in data science language: Python, R, or Matlab. Python is a plus. Knowledge of good code development practices (unit/integration testing, continuous integration, deployment, etc.) is also a plus.
• Knowledge of machine learning fundamentals and evaluation of machine learning models. Knowledge of deep learning frameworks like Pytorch or Tensorflow is a plus.
• Experience working with mass spectrometry, microscopy, and/or transcriptomics data is a plus, but not required.
• Web development skills are a plus, but not required.
• Candidates will be asked to work simultaneously on multiple projects (including internal and external collaborations) and will need to have a strong willingness to learn additional research skills when needed.
• Willingness to interface with wet lab and instruments to design computational approaches to data collection in addition to data analysis.

Please contact Jeff Spraggins at if you are interested in learning more about postdoctoral training opportunities in our research group.

Vanderbilt University is committed to the acceptance of a diverse group of trainees that is populated with people of all races, ethnicities, genders, gender identities, sexual orientations, disabilities, all places of geographic origin, and the full spectrum of socio-economic status.

Jeff Spraggins
: (615) 343-7333
2022-09-26 19:10:39

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