<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";">Hi Es_jobs_net,</div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";"><br class=""></div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";">I have a new postdoc opening: Can you please distribute the following job ad to your list?</div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";"><br class=""></div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";">Many thanks,</div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";"><br class=""></div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";">Mike.</div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";"><br class=""></div><div class="" style="margin: 0px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";">----</div><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">Postdoctoral position in Machine Learning of sub-grid microphysical and turbulent processes for next-gen climate modelling.<o:p class=""></o:p></span></b></p><p class="MsoNormal" style="text-align: justify;">The Department of Earth System Science at the University of California, Irvine is seeking a motivated atmospheric scientist / machine-learning (ML) enthusiast to engage in collaborative work between Mike Pritchard's research group (<a href="http://sites.ps.uci.edu/pritchard" class="">Computational Clouds and Climate Lab</a>) and an exciting group of DOE collaborators at the Pacific Northwest National Laboratory. </p><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">Position perks:<o:p class=""></o:p></span></b></p><p class="MsoListParagraphCxSpFirst" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Annual salary: $55k-70k USD + quality University of California medical, dental, and retirement benefits.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Flexible start date, through Fall 2021. <o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Remote work optional; international possible with approval.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">One year initially but renewable for a second, pending good progress & funding.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpLast" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Supportive department: excellent administrative support, postdoctoral community, early-career Slack.<o:p class=""></o:p></span></p><div style="text-align: justify;" class=""><span lang="EN" class=""> </span><br class="webkit-block-placeholder"></div><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">Your qualifications:<o:p class=""></o:p></span></b></p><p class="MsoListParagraphCxSpFirst" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">PhD in Atmospheric science and familiarity with parameterization issues.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Technically proficient in python, managing large datasets, some machine learning (ML).<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Comfortable working on remote clusters, shell scripting, general data wrangling, etc.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpLast" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Independent, collegial and open to multi-institutional collaborations.<o:p class=""></o:p></span></p><div style="text-align: justify;" class=""><span lang="EN" class=""> </span><br class="webkit-block-placeholder"></div><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">Perks of our group:<o:p class=""></o:p></span></b></p><p class="MsoListParagraphCxSpFirst" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Computing: Millions of CPU-hours, tens of thousands of GPU-hours, as needed.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Collaboration: Internally with 3 PhD students and 4 senior staff plus external networking with friendly colleagues working on similar themes at Columbia, UW, DLR, and MIT.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">Technical training opportunities: <o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">(By Prof. Michael Pritchard): High-resolution climate modeling, high-performance computing, cloud superparameterization, coupling neural networks to climate models, overview of ML for climate science.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">(By the group): Basic ML learning workflows for training and tuning feed-forward neural networks and variational autoencoders, incorporating physical constraints.<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpMiddle" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">(via PNNL collaborators): Next-gen aerosol and microphysics parameterization, large-eddy simulation (LES).<o:p class=""></o:p></span></p><p class="MsoListParagraphCxSpLast" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">(via UCI CS collaborator Professor Stephan Mandt): Co-mentoring on ML methods such as variational auto-encoding (VAE) for generative stochastic parameterization, data compression and associated latent space inquiry / anomaly detection.<o:p class=""></o:p></span></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN" class=""> <o:p class=""></o:p></span></p><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">Project description:</span></b></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN" class="">We are seeking an ambitious early career scientist interested in pushing frontiers of data-driven parameterization. The idea is to inform strategies for representing sub-4-km physics within a next generation of global cloud resolving models, focusing especially on microphysics-turbulence interactions. Some examples of specific problems of interest include:</span></p><p class="MsoListParagraph" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">How many statistical moments are enough for higher order closure (HOC) parameterization? This appears objectively testable via ML auto-encoding of Large Eddy Simulation (LES) data, given enough of it?</span> </p><p class="MsoListParagraph" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">A timely “interpretable AI” opportunity: Can clustering the latent space of variational autoencoders trained to compress such data provide insight to the conventional heuristic parameterization developer about which sub-regimes deserve formal separation?</span> </p><p class="MsoListParagraph" style="text-align: justify; text-indent: -0.25in;"><span lang="EN" class="" style="font-family: Symbol;"><span class="Apple-tab-span" style="white-space: pre;"> </span>·<span class="" style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><span lang="EN" class="">To what extent can ML-based closure schemes fit the most challenging pressure perturbation covariances that rely on especially ill-defined closure in HOC parameterization schemes? <o:p class=""></o:p></span></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN" class=""> </span>The work is to be in close collaboration with a group of established experts in microphysics, higher-order closure parameterization, and large-eddy simulation, based at or affiliated with the US Department of Energy’s Pacific Northwest National Laboratory, Sandia National Laboratories, University of Wisconsin-Milwaukee, University of Washington, University of Arizona, and Texas A&M University. Funding is through the auspices of DOE’s EAGLES project (Enabling Aerosol-cloud interactions at Global convection-permitting scales). Novel ML training data exist through this effort, including large eddy simulation ensembles optionally coupled to spectral bin microphysics.</p><p class="MsoNormal" style="text-align: justify;"><span lang="EN" class="">Please apply online at </span><a href="https://recruit.ap.uci.edu/apply/JPF06681" class="">https://recruit.ap.uci.edu/apply/JPF06681</a> with a cover letter that also describes your immediate and long-term research goals, a curriculum vitae including publications list, and names for three letters of reference (please do not solicit letters).</p><div style="text-align: justify;" class=""><span lang="EN" class=""> </span><br class="webkit-block-placeholder"></div><p class="MsoNormal" align="center" style="text-align: center;"><span lang="EN" class=""><img v:shapes="image1.png" apple-inline="yes" id="85450340-B2C9-424E-916B-8CF587ABEE7F" class="" width="277" height="184" src="cid:clip_image002.jpg"></span><span lang="EN" class=""><o:p class=""></o:p></span></p><div style="text-align: center;" class=""><span lang="EN" class=""> </span><br class="webkit-block-placeholder"></div><p class="MsoNormal" style="text-align: justify;"><b class=""><span lang="EN" class="">UC Irvine</span></b><span lang="EN" class=""> is located about halfway between Los Angeles and San Diego in coastal Southern California. It’s a nice place to live, not far from good beaches, and in a Mediterranean climate.</span> </p><p class="MsoNormal" style="text-align: justify;"><span lang="EN" class="">The Earth System Science Department at UC Irvine is a highly interdisciplinary environment comprising ~ 25 faculty with expertise across many components of the Earth System, including atmospheric and climate dynamics, land surface processes, terrestrial and marine biogeochemical cycles, ice sheets, and human systems. The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy.</span></p></body></html>