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Leila Character
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10 months ago
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Geography, Environmental Sciences Texas A&M University in United States
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
Aug 1, 2026
Country
United States
University
Texas A&M University
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Keywords
Computer Science
Environmental Science
Deep Learning
Remote Sensing
Geography
Spatial Analysis
Archaeology
Python Programming
Geographic Information System
Environmental Quality
Geospatial Information
About this position
#
PhD
Opportunity at Texas A&M University
Dr. Leila Character is seeking a creative problem solver PhD student to join her lab at Texas A&M University, Department of Geography, starting in Fall 2026.
The successful candidate will work on projects closely aligned with Dr. Character's expertise, focusing on collection, manipulation, and preprocessing of remotely sensed and training data to enable production of new information; development and application of deep learning models for object detection and segmentation using high-resolution remotely sensed data; and geospatial and spatial statistical analyses. Potential research areas include:
· Environmental Monitoring: Advancing methods for the detection, characterization, and modeling of natural and ecological phenomena with applications in the identification of environmental features, assessment of ecological health, and spatial characterization of terrestrial and marine environments.
· Geospatial Intelligence: Developing approaches for a diverse set of problems related to automatic target recognition (ATR), including remote sensing data collection, preprocessing, and fusion; machine learning model development and implementation; and human-in-the-loop decision-making systems.
· Archaeological Machine Learning: Developing deep learning and remote sensing approaches for the detection, mapping, and analysis of archaeological and cultural heritage features in terrestrial and underwater environments; integrating data from lidar, sonar, and other sensing modalities to advance heritage preservation, landscape analysis, and repatriation efforts.
The student's research will leverage diverse datasets and state-of-the-art machine learning frameworks contributing to both theoretical advancements and real-world problem-solving.
Required Qualifications:
• A Master's degree in Geography, Environmental Sciences, or a closely related/relevant field.
• Demonstrated proficiency in Python programming for machine learning (e.g., TensorFlow, Keras, PyTorch, Scikit-Learn).
• Strong skills in Geographic Information Systems (GIS) software (e.g., ArcGIS Pro, QGIS) and remote sensing data processing and analysis.
• Experience with and understanding of deep learning and other machine learning algorithms for feature detection.
• A strong interest in interdisciplinary research and the application of advanced geospatial techniques to complex real-world problems.
Interested candidates are strongly encouraged to review Professor Character's CV and recent publications to understand the scope and nature of the lab's research. To express interest, please send an email to [email protected] with the subject line "PhD Application - Geospatial Machine Learning" including:
1. Your Curriculum Vitae (CV).
2. A short statement of interest (a couple of paragraphs in the email) outlining your research experience, your specific interests that align with Professor Character's work, and your long-term academic and career goals.
The successful candidate will work on projects closely aligned with Dr. Character's expertise, focusing on collection, manipulation, and preprocessing of remotely sensed and training data to enable production of new information; development and application of deep learning models for object detection and segmentation using high-resolution remotely sensed data; and geospatial and spatial statistical analyses. Potential research areas include:
· Environmental Monitoring: Advancing methods for the detection, characterization, and modeling of natural and ecological phenomena with applications in the identification of environmental features, assessment of ecological health, and spatial characterization of terrestrial and marine environments.
· Geospatial Intelligence: Developing approaches for a diverse set of problems related to automatic target recognition (ATR), including remote sensing data collection, preprocessing, and fusion; machine learning model development and implementation; and human-in-the-loop decision-making systems.
· Archaeological Machine Learning: Developing deep learning and remote sensing approaches for the detection, mapping, and analysis of archaeological and cultural heritage features in terrestrial and underwater environments; integrating data from lidar, sonar, and other sensing modalities to advance heritage preservation, landscape analysis, and repatriation efforts.
The student's research will leverage diverse datasets and state-of-the-art machine learning frameworks contributing to both theoretical advancements and real-world problem-solving.
Required Qualifications:
• A Master's degree in Geography, Environmental Sciences, or a closely related/relevant field.
• Demonstrated proficiency in Python programming for machine learning (e.g., TensorFlow, Keras, PyTorch, Scikit-Learn).
• Strong skills in Geographic Information Systems (GIS) software (e.g., ArcGIS Pro, QGIS) and remote sensing data processing and analysis.
• Experience with and understanding of deep learning and other machine learning algorithms for feature detection.
• A strong interest in interdisciplinary research and the application of advanced geospatial techniques to complex real-world problems.
Interested candidates are strongly encouraged to review Professor Character's CV and recent publications to understand the scope and nature of the lab's research. To express interest, please send an email to [email protected] with the subject line "PhD Application - Geospatial Machine Learning" including:
1. Your Curriculum Vitae (CV).
2. A short statement of interest (a couple of paragraphs in the email) outlining your research experience, your specific interests that align with Professor Character's work, and your long-term academic and career goals.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
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