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<h3><span>AI Engineer / Bioinformatician – AI for Drug Discovery</span></h3>
<p class="isSelectedEnd"><span>Join the </span><strong><span>AI for
Genome Interpretation (AI4GI)</span></strong><span> group at </span><strong><span>IGMM-CNRS,
Montpellier</span></strong><span>, for a 13-month engineer
position with the possibility of renewal for up to 3 additional
years.</span></p>
<p class="isSelectedEnd"><span>You will work at the intersection of
</span><strong><span>machine learning, bioinformatics,
chemoinformatics and cancer biology</span></strong><span>, in
a collaboration between Raimondi and Hahne laboratories. The
goal is to develop AI methods to predict drug responses in </span><strong><span>treatment-resistant
CMS4 colorectal cancer</span></strong><span> and accelerate
the discovery of new therapeutic molecules.</span></p>
<p class="isSelectedEnd"><span>Your work will include:</span></p>
<ul data-spread="false">
<li><span>Developing </span><strong><span>novel PyTorch-based
machine learning methods</span></strong><span> for
drug-response prediction.</span></li>
<li><span>Integrating </span><strong><span>pharmacogenomic,
molecular and phenotypic data</span></strong><span> from
resources such as GDSC, DepMap and CCLE.</span></li>
<li><span>Developing molecular representations using </span><strong><span>chemoinformatics,
molecular fingerprints and graph-based approaches</span></strong><span>.</span></li>
<li><span>Building an </span><strong><span>Active Learning,
experiment-in-the-loop framework</span></strong><span> that
selects the most informative compounds for experimental
validation.</span></li>
<li><span>Applying the resulting models to the </span><strong><span>virtual
screening of 78 billion compounds</span></strong><span> in
the Enamine REAL chemical library.</span></li>
<li><span>Working closely with experimental cancer biologists to
iteratively improve the computational models.</span></li>
</ul>
<p class="isSelectedEnd"><span>We are looking for someone with
strong </span><strong><span>Python and machine learning skills</span></strong><span>,
a solid understanding of </span><strong><span>mathematics,
statistics and deep learning</span></strong><span>, and
familiarity with scientific computing and Linux. Experience in </span><strong><span>bioinformatics,
chemoinformatics, pharmacogenomics, drug-response prediction,
GNNs, Active Learning, uncertainty estimation or HPC/GPU
computing</span></strong><span> is a strong advantage.</span></p>
<p class="isSelectedEnd"><span>This is an opportunity to develop </span><strong><span>new
AI methodologies with direct applications to drug discovery</span></strong><span>,
while working in a highly interdisciplinary and international
research environment at IGMM-CNRS in Montpellier.</span></p>
<p><strong><span>Contract:</span></strong><span> 13 months,
renewable up to 3 years</span><br>
<strong><span>Salary:</span></strong><span> depending on
experience, from around €2,521 gross/month</span><br>
<strong><span>Benefits:</span></strong><span> 44 days annual
leave/RTT, remote-working support, 75% public transport
reimbursement and mobility allowance up to €300</span></p>
<p><span><br>
</span></p>
<p><span><b><u>Candidates can apply at this link</u></b> <a
moz-do-not-send="true"
href="https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-117/Default.aspx"
class="moz-txt-link-freetext">https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-117/Default.aspx</a></span></p>
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