<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=us-ascii">
<meta name="Generator" content="Microsoft Word 15 (filtered medium)">
<style><!--
/* Font Definitions */
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0in;
margin-bottom:.0001pt;
line-height:115%;
font-size:11.0pt;
font-family:"Arial",sans-serif;}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:#0563C1;
text-decoration:underline;}
p.MsoNoSpacing, li.MsoNoSpacing, div.MsoNoSpacing
{mso-style-priority:1;
margin:0in;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Arial",sans-serif;}
span.EmailStyle17
{mso-style-type:personal-compose;
font-family:"Calibri",sans-serif;
color:windowtext;}
.MsoChpDefault
{mso-style-type:export-only;
font-family:"Calibri",sans-serif;}
@page WordSection1
{size:8.5in 11.0in;
margin:1.0in 1.0in 1.0in 1.0in;}
div.WordSection1
{page:WordSection1;}
/* List Definitions */
@list l0
{mso-list-id:1774935521;
mso-list-template-ids:1992833346;}
@list l0:level1
{mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:1.0in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level2
{mso-level-number-format:alpha-lower;
mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:1.5in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level3
{mso-level-number-format:roman-lower;
mso-level-tab-stop:none;
mso-level-number-position:right;
margin-left:2.0in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level4
{mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:2.5in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level5
{mso-level-number-format:alpha-lower;
mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:3.0in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level6
{mso-level-number-format:roman-lower;
mso-level-tab-stop:none;
mso-level-number-position:right;
margin-left:3.5in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level7
{mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:4.0in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level8
{mso-level-number-format:alpha-lower;
mso-level-tab-stop:none;
mso-level-number-position:left;
margin-left:4.5in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
@list l0:level9
{mso-level-number-format:roman-lower;
mso-level-tab-stop:none;
mso-level-number-position:right;
margin-left:5.0in;
text-indent:-.25in;
mso-text-animation:none;
text-decoration:none;
text-underline:none;
text-decoration:none;
text-line-through:none;}
ol
{margin-bottom:0in;}
ul
{margin-bottom:0in;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]-->
</head>
<body lang="EN-US" link="#0563C1" vlink="#954F72">
<div class="WordSection1">
<p class="MsoNoSpacing"><b><span lang="EN">Post-doc position: Machine Learning & Value of Information for Battery Metals Exploration
<br>
<br>
</span></b><b><span lang="EN" style="font-size:10.0pt">Principal Investigator <br>
</span></b><span lang="EN" style="font-size:10.0pt">Jef Caers, Professor of Geological Sciences, Stanford University
<o:p></o:p></span></p>
<p class="MsoNoSpacing"><span lang="EN" style="font-size:10.0pt">Director, Stanford Center for Earth Resource Forecasting</span><span lang="EN"><br>
<br>
<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:105%"><b><span lang="EN" style="font-size:10.0pt;line-height:105%">Sponsoring Company</span></b><span lang="EN" style="font-size:10.0pt;line-height:105%"><br>
KoBold Metals<br>
<br>
<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-align:justify;line-height:105%">
<span lang="EN" style="font-size:10.0pt;line-height:105%">We are seeking a postdoctoral researcher for a 2-year position to research the application of machine-learning driven Value-of-Information (VOI) to mineral exploration in the near-mine environment. The
candidate will collaborate directly with </span><span lang="EN"><a href="https://www.koboldmetals.com/"><span style="font-size:10.0pt;line-height:105%;color:#1155CC">KoBold Metals</span></a></span><span lang="EN" style="font-size:10.0pt;line-height:105%">
(KoBold), a San Francisco Bay Area company that is developing artificial intelligence to improve the efficacy and efficiency of mineral exploration. The collaboration with KoBold will focus on the near-mine environment, and it will provide data from existing
mines, as well as undeveloped deposits, to enable the application of machine learning to resource expansion exploration. This research will investigate how the VOI decision-theoretic can optimize and guide mineral exploration, in the near-mine environment,
by rigorously determining how new data collection will improve predictive power.
<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-align:justify;line-height:105%">
<span lang="EN" style="font-size:10.0pt;line-height:105%">This research will require a disciplinary background in data science, including experience with geospatial data; further background/training in the broader geosciences will be useful. The candidate will
also need experience with databases (SQL, etc.) and Python scripting. In order of importance, we are looking for candidates with:<o:p></o:p></span></p>
<ol style="margin-top:0in" start="1" type="1">
<li class="MsoNormal" style="margin-left:.5in;text-align:justify;line-height:105%;mso-list:l0 level1 lfo1">
<span lang="EN" style="font-size:10.0pt;line-height:105%">Excellence in research as demonstrated through publications in international journals<o:p></o:p></span></li><li class="MsoNormal" style="margin-left:.5in;text-align:justify;line-height:105%;mso-list:l0 level1 lfo1">
<span lang="EN" style="font-size:10.0pt;line-height:105%">Demonstrated computer science expertise in data science programming, big data manipulation, and cloud computing.<o:p></o:p></span></li><li class="MsoNormal" style="margin-left:.5in;text-align:justify;line-height:105%;mso-list:l0 level1 lfo1">
<span lang="EN" style="font-size:10.0pt;line-height:105%">Having a dual data science / geoscience background in research or application<o:p></o:p></span></li><li class="MsoNormal" style="margin-left:.5in;text-align:justify;line-height:105%;mso-list:l0 level1 lfo1">
<span lang="EN" style="font-size:10.0pt;line-height:105%">Background in geostatistics and geophysics<o:p></o:p></span></li><li class="MsoNormal" style="margin-left:.5in;text-align:justify;line-height:105%;mso-list:l0 level1 lfo1">
<span lang="EN" style="font-size:10.0pt;line-height:105%">Having worked on real data with real practical impact<o:p></o:p></span></li></ol>
<p class="MsoNormal" style="text-align:justify"><span lang="EN" style="font-size:10.0pt;line-height:115%"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><b><span lang="EN" style="font-size:10.0pt;line-height:115%">Summary of research project<o:p></o:p></span></b></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-align:justify;line-height:106%">
<span lang="EN" style="font-size:10.0pt;line-height:106%">Data-driven exploration relies on geophysical, geochemical, and geological data to develop mineral potential maps. The historic focus of this type of work has been on developing such maps, rather than
on how such maps could be used to guide exploration activities. Further, these prospectivity mapping exercises have a poor track record for a variety of reasons. First, uncertainty is seldom, if ever, properly carried through the assessment, and as such, it
is not possible to quantify a map’s reliability. Second, even if reliability were rigorously quantified, what should be done next? Should more data be collected? If so, where? And, what kind?<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-align:justify;line-height:106%">
<span lang="EN" style="font-size:10.0pt;line-height:106%">In brown field exploration, when a significant resource has already been identified through drilling and high-resolution data collection, the key question becomes: can the resource be cost-effectively
enlarge by near field exploration? Again, what type of data should be collected and where? Is the statistically-valid expected value of the incremental resource greater than the expected cost of collecting the data? Is that right trade off?
<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-align:justify;line-height:106%">
<b><u><span lang="EN" style="font-size:10.0pt;line-height:106%">Send a CV, statement and a list of three referees to jcaers@stanford.edu<o:p></o:p></span></u></b></p>
<p class="MsoNormal"><span style="font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN"><o:p> </o:p></span></p>
</div>
</body>
</html>