<html><head></head><body><div><div><p class="p1" style="margin:0px;"><b class="">PhD in Hydrological Predictions to Landslides, Due <span class="sh-date">Oct 30</span> for Spring 2027, Rice University</b><br/></p><div class=""><br/></div><p class="p1" style="margin:0px;">The <i class="">Computational Hydrology and Remote Sensing</i> research group at <a class="" href="https://www.rice.edu/" rel="noopener noreferrer"><span class="s1">Rice University</span></a><span class="s1"> </span>is seeking a PhD candidate to develop high-resolution, physics-informed landslide-risk models that account for hydrological preconditions through the integration of AI, hydrological modeling, and satellite data fusion. This project will support the USDA Forest Service's CulvertApp (<a class="" href="https://culvert-at-risk.org/">https://culvert-at-risk.org/</a>) in partnership with the Department of Transportation. More information: <a class="" href="http://www.waterai.earth/">www.waterai.earth</a><br/></p><p class="p2" style="margin:0px;"><br/></p><p class="p1" style="margin:0px;">Application deadline: <b class=""><span class="sh-date">October 30, 2026</span>.</b><br/></p><p class="p3" style="margin:0px;"><br/></p><p class="p4" style="margin:0px;"><span class="s2"><b class="">Background:</b></span><span class="s3"><b class=""><br/> </b></span>Successful candidates will have a background in geosciences, environmental science and engineering, climate sciences, applied math, physics, scientific deep learning, or related fields. This is an excellent opportunity to develop expertise in land surface modeling, hydrologic prediction, satellite remote sensing, and data science.</p><p class="p5" style="margin:0px;"><br/></p><p class="p4" style="margin:0px;"><b class="">Essential Qualifications: </b><br/></p><ul class="ul1" type="disc"><li class="li1" aria-level="1" style="list-style-type:disc">Strong analytical skills, ability to think critically and solve problems effectively.<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">A solid understanding of calculus and numerical methods.<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Strong programming skills (UNIX/Linux, Python, shell scripting) for processing and visualization of simulation and remote sensing data.<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Experience with geospatial datasets (e.g., GeoTIFF, NetCDF, HDF5, Zar, dask) and data processing using Python geospatial libraries (e.g., xarray, cartopy, rasterio).<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Experience with machine learning and/or deep learning concepts using Python (e.g., PyTorch, TensorFlow)<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Eagerness to acquire new skills and adapt them to cutting-edge research in hydrology and Earth sciences.<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Excellent written and verbal communication.<br/></li></ul><p class="p4" style="margin:0px;"><b class="">Desired Qualifications:</b><br/></p><ul class="ul1" type="disc"><li class="li1" aria-level="1" style="list-style-type:disc">Demonstrate a research track record of involvement in topics relevant to computational hydrology, remote sensing, or related fields.<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Hands-on experience with machine learning or deep learning frameworks (e.g., TensorFlow, PyTorch).<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Familiarity with High-performance computing (HPC) systems and/or cloud computing (e.g., AWS, Google Cloud, etc.).<br/></li><li class="li1" aria-level="1" style="list-style-type:disc">Familiarity with version control systems for collaborative code development (e.g., Github, Gitlab)<br/></li></ul><p class="p2" style="margin:0px;"><br/></p><p class="p4" style="margin:0px;">All researchers will benefit from our group's involvement in national and international collaborative projects, and Rice's thriving and expanding programs, such as the <a class="" href="https://datascience.rice.edu/" rel="noopener noreferrer"><span class="s1">Rice Data Science Initiative</span></a>, <a class="" href="https://d2k.rice.edu/" rel="noopener noreferrer"><span class="s1">Data to Knowledge Lab</span></a>, and the <a class="" href="https://kenkennedy.rice.edu/" rel="noopener noreferrer"><span class="s1">Ken Kennedy Institute</span></a>.<br/></p><p class="p4" style="margin:0px;"><b class="">Application for PhD students:<span class="Apple-converted-space"> </span></b><br/></p><p class="p4" style="margin:0px;"><b class=""><br/></b>PhD students should submit an application to <a class="" href="https://eeps.rice.edu/graduate/phd-program" rel="noopener noreferrer"><span class="s1">Earth, Environmental, and Planetary Sciences</span></a> (deadline of <b class=""><span class=""><span class="sh-date">October 30, 2026</span></span>.</b>). International students should also meet the <a class="" href="https://graduate.rice.edu/admissions/language-proficiency-requirements" rel="noopener noreferrer"><span class="s1">language proficiency requirements</span></a>. Prospective graduate students can email Dr. Vergopolan (<a class="" href="mailto:vergopolan@rice.edu">vergopolan@rice.edu</a>) with the subject "Prospective PhD student" before applying. In the email, please include your unofficial transcripts and curriculum vitae attached as a PDF and include <i class="">a brief personal statement explaining why you fit and why you would like to join the group</i>. We greatly appreciate all the applications, but due to the high volume of submissions, only shortlisted candidates will be notified. Compensation: $37K/year stipend with benefits plus full tuition ($66K/year).</p></div><div><div style="display: none; border: 0px; width: 0px; height: 0px; overflow: hidden; visibility: hidden;"><img src="https://r.superhuman.com/~dBIXjB0lSLqFqjiS_1MM3OL4KNo1HYCvL_zkkx0oHJqVabH7hJBUNTnyQMSLpwALhaMHf2lH37DNClBSqEQho9i4_MLjRytgAxb5hhU.gif" alt=" " width="1" height="0" style="display: none; border: 0px; width: 0px; height: 0px; overflow: hidden; visibility: hidden;"/><!-- --></div><br/><div class="gmail_signature"><div><div dir="ltr"><font class="sh-preserve-color sh-preserve-font-family" color="#666666" face="trebuchet ms, sans-serif"><b class="sh-preserve-color sh-preserve-font-family">Dr. Noemi Vergopolan</b><br class="sh-preserve-color sh-preserve-font-family"/>Assistant Professor of Computational Hydrology and Remote Sensing</font><div><font class="sh-preserve-color sh-preserve-font-family" color="#666666" face="trebuchet ms, sans-serif">Earth, Environmental, and Planetary Sciences</font></div><div><font class="sh-preserve-color sh-preserve-font-family" color="#666666" face="trebuchet ms, sans-serif">Civil and Environmental Engineering (affiliated)</font></div><div><font class="sh-preserve-color sh-preserve-font-family" color="#666666" face="trebuchet ms, sans-serif">Rice University | Houston, TX<br class="sh-preserve-font-family"/>Email: <a class="sh-preserve-font-family" href="mailto:vergopolan@rice.edu" rel="noopener noreferrer" target="_blank">vergopolan@rice.edu</a><br class="sh-preserve-font-family"/>Website: <a class="sh-preserve-font-family" href="http://www.waterai.earth" rel="noopener noreferrer" target="_blank">www.waterai.earth</a><br class="sh-preserve-font-family"/>LinkedIn: <a class="sh-preserve-font-family" href="https://www.linkedin.com/in/vergopolan/" rel="noopener noreferrer" target="_blank">@vergopolan</a></font></div></div></div><br/></div></div></div></body></html>