Postdoctoral Researcher; Machine Learning

Position: Postdoctoral Researcher (Machine Learning)
Location: Prairie View

* Develop, test, and optimize machine learning and deep learning models for hyperspectral plant imaging, including CNNs, UNets, Res Nets, Dense Nets, Vision Transformers, Autoencoders and Variational Autoencoders, Generative Adversarial Networks, and Graph Neural Networks.
* Improve traditional spectral only analysis methods used in Multivariate Curve Resolution (MCR) by applying approaches that use both spatial and spectral information.
* Process, clean, and curate hyperspectral data collected with HCFM microscopes, and develop reproducible data processing and workflow tools.
* Explore alternative algorithms and data driven approaches to enhance pigment localization and pigment classification accuracy.
* Maintain clear documentation of model architecture, workflows, code, experiments, and results.
* Provide leadership within the research group by taking ownership of project components and mentoring junior researchers.
* Supervise undergraduate and graduate students in machine learning concepts, data analysis, experimental planning, and scientific writing.
* Train new group members on computational tools, machine learning best practices, and research methodologies.
* Assist the principal investigator with the preparation and submission of manuscripts, patent applications, and research proposals.
* Present research outcomes at internal meetings, seminars, conferences, and collaborative review sessions.
* Participate in departmental or college-wide events, committees, and performs other duties as assigned.
* Ph.D. degree in Computer Science, Electrical or Computer Engineering, Data Science, Computational Biology, Applied Mathematics, or a related discipline.
* At least one year of experience applying machine learning or data driven methods in research settings.
* Strong programming skills in Python and experience with machine learning libraries such as PyTorch, Tensor Flow, scikit learn, and Keras.
* Knowledge of machine learning, deep learning, data analytics, and model evaluation.
* Experience with data processing pipelines, statistical analysis, and data visualization tools including matplotlib, seaborn, and Plotly.
* Strong written and verbal communication abilities and the ability to collaborate with multidisciplinary teams.
* A record of contributing to peer reviewed publications.
* Two or more years of experience applying machine learning to scientific imaging or engineering data.
* Familiarity with image processing or hyperspectral imaging, preferably using HCFM or similar systems.
* Background knowledge in plant physiology, photosynthesis, or pigment biochemistry.
* Experience with high performance computing, Linux environments, and version control systems such as Git.
* Prior experience mentoring or supervising students.
* Experience contributing to successful research funding proposals.
** Background Check Requirements:
** For more than 140 years, Prairie View A&M University (PVAMU) has been igniting passion in its students. With an established reputation for producing engineers, nurses, and educators, PVAMU offers baccalaureate degrees, master’s degrees, and doctoral degree programs through nine colleges and schools. The University has an enrollment of more than 8,500 students. Designated an institution of “the first class” in the Texas Constitution, PVAMU is the second-oldest public institution of higher education in the state and a member of The Texas A&M University System.

The University is dedicated to fulfilling its land-grant mission of achieving excellence in teaching, research, and service.
PVAMU is conveniently located near the rapidly growing residential and commercial area known as the Northwest Houston Corridor. The developing Northwest Houston Corridor offers newly developed suburban communities, a variety of shopping and dining near the Houston Premium Outlets and is a short, 35-minute commute into downtown Houston.
#J-18808-Ljbffr