<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:0cm;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Calibri",sans-serif;}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:#0563C1;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:#954F72;
text-decoration:underline;}
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:612.0pt 792.0pt;
margin:72.0pt 72.0pt 72.0pt 72.0pt;}
div.WordSection1
{page:WordSection1;}
--></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="MsoNormal" style="line-height:24.0pt;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;font-family:"Arial",sans-serif;color:#434649">Post-doc position Machine Learning for Drug Discovery<o:p></o:p></span></p>
<p class="MsoNormal" style="line-height:24.0pt;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;font-family:"Arial",sans-serif;color:#434649">Key words: data sciences, machine learning, drug discovery<o:p></o:p></span></p>
<p class="MsoNormal" style="line-height:24.0pt;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;font-family:"Arial",sans-serif;color:#434649"><o:p> </o:p></span></p>
<p class="MsoNormal" style="line-height:24.0pt;background:white;vertical-align:baseline">
<u><span style="font-size:10.0pt;font-family:"Arial",sans-serif;color:#434649">Context<o:p></o:p></span></u></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify;line-height:150%;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">The brand Janssen (referring to Dr. Paul Janssen, founder of Janssen Pharmaceutica) now unites all pharmaceutical companies under the umbrella of Johnson and Johnson,
one of the biggest health care companies in the world. Small molecule research remains a core activity in brining innovative drugs to patients, and this activity is strongly represented at the Beerse flagship site of Janssen’s BeNeLux Campus. Over the last
few years, Janssen has invested substantially in virtualizing part of the research process by introducing large scale machine learning to leverage the extensive datasets accessible to the company: chemical descriptions and biological activity annotations for
millions of compounds across thousands of assays, and information rich but hard-to-interpret compound documentation like microscopy images or transcriptomics profiles at full industrial scale. The Beerse site has been pivotal in this move. For instance, it
has initiated more than half a dozen active collaborative research projects across Europe, involving satellite European sites where helpful.<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify;line-height:150%;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">In this context, Janssen is looking for machine learning candidates with a strong interest in industry-academic collaborative research with secured funding, to actively
participate in the development, refinement and deployment of machine learning approaches in small molecule research. This first position concerns publishable machine learning research, collaborating with both academic partners and industrial colleagues that
will apply the research. The successful candidate has the opportunity to grow experience in industrially relevant research in a field where pharma is poised to be actively recruiting for years to come and be exposed to two major pharmaceutical companies. Janssen
is planning to open additional post-doc positions during the year, and therefore also encourages open applications from post-docs qualified in this field. If you are a driven and versed machine learner, open to the application of your skills in a noble industry
– don’t hesitate to reach out.<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify;line-height:150%;background:white;vertical-align:baseline">
<u><span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">Job description<o:p></o:p></span></u></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify;line-height:150%;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">A fully funded 1-year post-doc position, starting March 1 2018, or as soon as possible thereafter, starting in the Janssen site just outside of the beautiful medieval
city of Toledo, Spain connected to Madrid by a 37-minute train ride, for 6 months. The second half of the post-doc can be arranged at the preference of the successful candidate, in Toledo, Spain or in Beerse, Belgium (about a 30-minute drive east of the main
Belgian port city of Antwerp). The post-doc will collaborate in the context of the 9 partner H2020 project ExCAPE (</span><a href="http://excape-h2020.eu/"><span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif">http://excape-h2020.eu/</span></a><span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">)
that aims to harness high-performance computing and machine learning (SVM, matrix factorization, deep learning, auto-encoders) for drug discovery. During the second half of the project, the post-doc will be involved in applying the learnings and software from
ExCAPE to active industrial discovery projects, informing the selection of chemistry to pursue.<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify;line-height:150%;background:white;vertical-align:baseline">
<span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649">For further information: contact
</span>hceulema@its.jnj.com<span style="font-size:10.0pt;line-height:150%;font-family:"Arial",sans-serif;color:#434649"><o:p></o:p></span></p>
</div>
</body>
</html>