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<p class="MsoNormal"><b><span style="font-size:14.0pt">Ph.D. Opportunities in Hydrologic Modeling and Scientific Machine Learning<o:p></o:p></span></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I am seeking highly motivated Ph.D. students to join <a href="https://villarini.scholar.princeton.edu/">
our research group</a> in the <a href="https://cee.princeton.edu/">Department of Civil and Environmental Engineering</a> and the<a href="https://environment.princeton.edu/"> High Meadows Environmental Institute</a> at Princeton University in Fall 2027. Research
in the group focuses on advancing hydrologic prediction through improved process understanding, model development, and scientific machine learning approaches. Current opportunities center on the development of the Tiger-HLM modeling framework and span multiple
areas of hydrology, Earth system science, and scientific machine learning.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Research Area 1: Hydrologic Modeling and Earth System Processes<o:p></o:p></b></p>
<p class="MsoNormal">Research in this area focuses on advancing large-scale hydrologic modeling through the development of improved process representations, parameterizations, and model formulations within Tiger-HLM. Research topics span both natural and human-influenced
hydrologic systems. Of particular interest are applications in Arctic and cold-region environments, including permafrost, glaciers, seasonal snow, and ice processes, as well as human-water interactions such as irrigation and water withdrawals. Additional interests
include improving the representation of hydrologic processes and streamflow prediction in arid and water-limited regions. These diverse hydrologic settings provide opportunities to test and improve process representations and model formulations, while the
broader goal is to advance hydrologic prediction across diverse hydroclimatic regimes and environmental conditions.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Research Area 2: Scientific Machine Learning for Hydrology<o:p></o:p></b></p>
<p class="MsoNormal">Research in this area focuses on the development and use of machine learning methods that are informed by physical principles and process-based models to improve hydrologic prediction, accelerate computation, and support process discovery.
Topics include machine-learning emulators for Tiger-HLM (e.g., neural operators and transformers) and interpretable machine learning, including symbolic regression, for the discovery of new parameterizations, process representations, and model formulations
that can be implemented and tested in Tiger-HLM. The goal is to develop computationally efficient, scientifically interpretable, and physically consistent tools that advance hydrologic prediction and model development.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Qualifications:</b> Candidates must have a degree in engineering, earth or environmental science, applied mathematics, computer science, physics, statistics, or a related discipline. Strong quantitative and computational skills are expected.
Experience with hydrologic modeling, machine learning, scientific programming, numerical methods, and/or analysis of large geophysical datasets is particularly desirable.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>How to Apply:</b> Submit your application via Princeton University’s
<a href="https://gradschool.princeton.edu/admission-onboarding/apply">application portal</a> by 21 December, 11:59 pm EST. For detailed application instructions and qualifications, refer to the
<a href="https://cee.princeton.edu/graduate/how-apply">department’s guidelines</a>.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Princeton University is an equal opportunity employer/affirmative action employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity
or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<b><span style="font-size:10.0pt;line-height:120%;font-family:"Arial",sans-serif;color:#E77500;border:none windowtext 1.0pt;padding:0in;background:white">_____________<o:p></o:p></span></b></p>
<p class="MsoNormal" style="vertical-align:baseline"><span style="font-size:14.0pt;font-family:"Georgia",serif;color:#2A668F;border:none windowtext 1.0pt;padding:0in"><o:p> </o:p></span></p>
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<span style="font-size:14.0pt;line-height:120%;font-family:"Georgia",serif;color:#2A668F;border:none windowtext 1.0pt;padding:0in">Gabriele Villarini</span><span style="font-size:10.5pt;line-height:120%;font-family:"Segoe UI",sans-serif;color:black"><o:p></o:p></span></p>
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<span style="font-size:10.0pt;line-height:120%;color:#333333;border:none windowtext 1.0pt;padding:0in">Vice Dean, School of Engineering and Applied Science<o:p></o:p></span></p>
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<span style="font-size:10.0pt;line-height:120%;color:#333333;border:none windowtext 1.0pt;padding:0in">Theodora Shelton Pitney Professor in Environmental Studies<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:1.2pt;line-height:120%;vertical-align:baseline">
<span style="font-size:10.0pt;line-height:120%;color:#333333;border:none windowtext 1.0pt;padding:0in">Professor,
</span><span style="font-size:10.0pt;line-height:120%;border:none windowtext 1.0pt;padding:0in">Department of Civil and Environmental Engineering and High Meadows Environmental Institute<o:p></o:p></span></p>
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<span style="font-size:10.0pt;line-height:120%;border:none windowtext 1.0pt;padding:0in">Princeton University</span><span style="font-size:10.5pt;line-height:120%;color:black"><o:p></o:p></span></p>
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<b><span style="font-size:10.0pt;line-height:120%;border:none windowtext 1.0pt;padding:0in"><a href="mailto:gvillari@princeton.edu"><span style="color:#2A668F">gvillari@princeton.edu</span></a></span></b><span style="font-size:10.5pt;line-height:120%;color:black"><o:p></o:p></span></p>
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<span style="font-size:10.0pt;line-height:120%;color:#333333;border:none windowtext 1.0pt;padding:0in">609-258<span style="background:white">-</span>3761</span><span style="font-size:11.0pt;line-height:120%"><o:p></o:p></span></p>
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