Job Description

Job Description

We are seeking a Full-Time Postdoctoral Researcher in the Department of Bioengineering, at The University of Texas at Arlington. The successful candidates will join the dynamic and interdisciplinary research group of Dr. Xi Zhu, focusing on analyzing neuroimaging data, developing novel methods for analysis and integration of multimodal neuroimaging, behavioral and clinical data, and using machine learning /artificial intelligence approaches to construct predictive network models in psychiatric disorders. The candidates will work closely with faculty in Psychology and Psychiatry Department at Duke U, Columbia U and UT-GRV for a collaborative R01 project on "Understanding Heterogeneity Across PTSD, MDD and Anxiety By Leveraging Large-scale Multimodal Neuroimaging Datasets".

Duties and Responsibilities:

• Assist with organizing large scale multimodal neuroimaging dataset, brain imaging quality control and processing
• Develop and optimize state-of-the-art models to extract actionable insights from complex, unstructured biomedical image data.
• Utilize high-performance computing resources and techniques to manage and process large-scale datasets and complex models efficiently.
• Clearly articulate complex technical methods and results to diverse audiences, including scientists, stakeholders, and decision-makers, to facilitate informed decision-making and drive project success.
• Assist with grant applications, manuscript preparation and supervision of RAs and volunteers

Minimum Qualifications: 

• Doctoral degree in biomedical engineering, electrical engineering, computer science, biostatistics, or a related field
• Experience in data science and quantitative research
• Experience in programming languages including Python or R
• Experience with various machine learning/deep learning techniques (including fully supervised, unsupervised, self-supervised, and weakly supervised learning) and specific architectures (Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and attention-based networks), along with optimization techniques.

Preferred Qualifications: 

• Experience in developing state-of-the-art AI/ML algorithms
• Experience with one or more deep learning libraries such as PyTorch, TensorFlow, or Keras.
• Hands-on experience with neuroimaging processing pipelines.
• Excellent writing and communication skills
• Strong interest in neuroscience and psychiatric research
• Proficiency in statistical analysis and data handling, including data augmentation, data cleaning, normalization, and handling large-scale image datasets.
• Ability to effectively communicate complex technical concepts and results to a wide range of audiences, including non-technical stakeholders.
• Highly motivated and capable of driving projects forward independently with minimal daily oversight.
• Experience working with multi-GPU workloads for training and inference
• A deep passion for leveraging data and AI to drive scientific innovation and improve healthcare outcomes.
• Proven scientific excellence as evidenced by publications, patents, and high-level presentations.
• Hands-on experience in data AI solutions with focus on neuroimaging data (MRI, CT, PET, fMRI).
• Experience with high-performance computing techniques and resources to manage and process large-scale datasets and complex models efficiently.

Application materials:

• CV, which lists all peer-reviewed publications/conferences and previous awards/grants received
• Research statement that outlines your research achievements and future research interests
• A journal article or book chapter that demonstrates your domain expertise
• Two reference letters

Work Location: SEIR 225, 655 W Mitchell St, Arlington, TX 76010

To Apply: All applicants should include a cover letter with their application. Applications without a cover letter will not be considered. Please send your application materials to kangyi.peng@uta.edu and xi.zhu@uta.edu

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