Research Assistant Scientist

Classification

Title:

Research Assistant Scientist Classification

Minimum Requirements:

* Ph.D. in Computer Science, Biomedical Informatics, Data Science, Biomedical Engineering, Electrical Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field.

* Demonstrated experience developing AI and machine learning models for biomedical applications.

Job Description:

The University of Florida Diabetes Institute (UFDI) invites applications for a full-time, non-tenure-track Assistant Scientist to join an interdisciplinary research environment focused on advancing the prevention, prediction, and treatment of diabetes through artificial intelligence, computational biology, and precision medicine. The successful candidate will contribute to the development and application of innovative AI and machine learning approaches that accelerate discovery across basic, translational, and clinical diabetes research

Research efforts may support initiatives such as:

* AI-enabled discovery of novel diabetes therapies

* Precision medicine for Type 1 and Type 2 diabetes

* Human pancreas imaging and spatial biology

* Digital pathology and computational tissue analysis

* Clinical decision support using EHR data

* Translational validation using human biospecimens and experimental model system

* Artificial intelligence and machine learning for diabetes research, including Type 1 and Type 2 diabetes

* Computational pathology and digital pathology using whole slide imaging (WSI)

* Spatial biology, spatial transcriptomics, and multi-omics data integration

* Large language models (LLMs) and foundation models for biomedical research

* Electronic Health Record (EHR) analytics and clinical data integration

* Biomedical image analysis and quantitative microscopy

* High-performance computing (HPC) and scalable AI pipelines

* Development of reproducible software tools and computational workflows for biomedical research

Develop novel AI, machine learning, and deep learning methods to address complex biomedical questions in diabetes.

Design and implement computational tools for integrating imaging, genomic, transcriptomic, proteomic, metabolomic, and clinical datasets.

Develop scalable software applications and maintain research code using modern software engineering practices.

Apply AI methods to whole slide images, microscopy datasets, spatial transcriptomics, and EHR-derived clinical data.

Collaborate with multidisciplinary teams of clinicians, computational scientists, engineers, statisticians, and laboratory investigators.

Lead and participate in collaborative research projects spanning basic science, translational research, and clinical applications.

Prepare scientific manuscripts, conference presentations, and competitive grant applications.

Mentor graduate students, postdoctoral fellows, research staff, and trainees.

Expected Salary:

Commensurate with education and experience

Required Qualifications:

* Ph.D. in Computer Science, Biomedical Informatics, Data Science, Biomedical Engineering, Electrical Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field.

* Demonstrated experience developing AI and machine learning models for biomedical applications.

* Strong programming experience in Python and/or R.

* Experience with Git/Git Hub and collaborative software development.

* Experience working in Linux and high-performance computing environments.

* Evidence of scholarly productivity through peer-reviewed publications.

Preferred:

* Experience applying AI or machine learning to diabetes, metabolic disease, immunology, or other complex biomedical diseases.

* Experience with digital pathology, computational pathology, whole slide image analysis, or quantitative microscopy.

* Experience integrating multi-modal datasets, including genomics, transcriptomics, spatial transcriptomics, proteomics, metabolomics, imaging, and EHR data.

* Experience with modern deep learning frameworks such as PyTorch, Tensor Flow, Keras, Scikit-learn, Pandas, Num Py, and Sci Py.

* Familiarity with convolutional neural networks (CNNs), graph neural networks (GNNs), transformer architectures, foundation…