Research Professor of Computational Science, Summer 2025

Description

The Institute for Earth, Computing, Human, and Observing (ECHO) at Chapman University invites applications for a Research Professor (Non -Tenure Track). This exciting opportunity for highly motivated individuals to join our interdisciplinary research team at ECHO is available beginning June 2025.  

Chapman University is a nationally ranked institution offering traditional undergraduate and graduate programs in the heart of Orange County, one of Southern California’s most diverse and vibrant regions. The University achieved R2 status in the Carnegie Classification of Institutions of Higher Education, a distinction held by just 10 percent of all U.S. universities and is ranked as #121 among Best National Universities by US News and World Report (2025). Our faculty include academic leaders who excel in research, publishing and world-class teaching in our 11 schools and colleges dedicated to forward-looking, personalized education, we create an environment for unlimited achievement by both our students and faculty.

The Institute for Earth, Computing, Human and Observing (ECHO) is an interdisciplinary research unit that focuses on the Earth and its systems. Scientific areas include natural hazards such as wildfires, severe weather, floods, air pollution, and earthquakes, and the changing Earth climate and its impacts on agriculture, economic factors, the atmosphere, and oceans. Areas of excellence also include central aspects of modern physics, such as quantum mechanics and the role of the mind, brain science, and the universe, in which planet Earth, humans, and all life are found. ECHO scientists use regional climate modeling, Earth observations, advanced data analysis, machine and deep learning, and spatial analysis to study the different environments.

We seek a computational scientist with a strong background in artificial intelligence (AI), machine learning, structural computational biology and bioinformatics. The successful candidate will contribute to research initiatives, mentor students, and collaborate on projects involving machine learning, neuroscience, molecular modeling, and AI-driven drug discovery.

The myriad areas of research among ECHO faculty, affiliated faculty, postdoctoral fellows, international collaborators, and students reflect the interdisciplinary nature of ECHO.  More information about Chapman University is available at www.chapman.edu. 

Responsibilities

This position reports to the Director of Institute for Earth, Computing, Human and Observing and will involve in research, student mentorship, and collaborative project development. The successful candidate is expected to:

1. Conduct and support research in neuroscience, molecular modeling, Computer based biology using AI and bioinformatics.
2. Develop and machine learning models for computer-based biology, finding new drugs, and analyzing biological data.
3. Analyze large-scale datasets using tools such as Schrödinger, MOE, and Amber.
4. Design and improve bioinformatics processes for high-throughput sequencing data analysis.
5. Utilize powerful computer systems like high-performance computing (HPC) clusters (CPU & GPU environments) for computational research.
6. Mentor undergraduate and graduate students in computational and data-driven research projects.
7. Collaborate with interdisciplinary research teams, including faculty from biological sciences, chemistry, and data science.
8. Publish research findings in high-impact scientific journals and presents at national and international conferences.
9. Contribute to the university’s research initiatives in AI applications for life sciences.

Qualifications

Required

1. Ph.D. in Computational Science, Computational Biology, or a related field.
2. At least 5 years of experience in Computational Science.
3. Extensive knowledge in AI-driven AI methods for bioinformatics and molecular modeling.
4. Expertise in analyzing large datasets and building predictive models.
5. Strong programming skills in Python, R, shell scripting, and GitHub.
6. Experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
7. Ability to create and run bioinformatics processes and molecular modeling methods like docking and molecular dynamics.
8. Experience with high-performance computing (HPC) clusters for large-scale data processing.
9. A strong record of peer-reviewed publications in relevant computational science fields.
10. Excellent communication and organizational skills for effective interdisciplinary collaboration.

Preferred

1. Postdoctoral research experience in computational biology, machine learning, or bioinformatics and securing external research funding.<