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<b>POSTDOCTORAL POSITION IN THE DEVELOPMENT OF MACHINE LEARNING
and
DEEP LEARNING METHODS GENETICS and BIOINFORMATICS</b></p>
<p align="left"
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
We are looking for a motivated postdoctoral researcher to join the
<a
href="https://www.igmm.cnrs.fr/team/ia-pour-linterpretation-du-genome/"><font
color="#1155cc"><u>AI
for Genome Interpretation (AI4GI</u></font></a>) group at
the IGMM
(CNRS, Montpellier) for <b>12 months</b>. The contract <b>can be
renewed for extra 36 months</b> if the project passes the
evaluation
steps.</p>
<p align="left"
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm"><b>Are
you a machine learning expert, proficient in programming
with tensors and vectorial operations (pytorch, numpy)? Do you
know
the </b><i><b>ins and outs</b></i><b> of machine learning
methods and
you can build neural networks from scratch? Do you enjoy
developing
new neural network architectures to solve non-conventional
problems?
This position might be for you!</b></p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
We are looking for a <b>motivated</b> and curious candidate, with
a
<b>strong background in the development of machine learning
methods
for bioinformatics. </b>
</p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
<u><b>Context:</b></u><b> </b>The position is based at the
Institute
of Molecular Genetics of Montpellier (IGMM, CNRS), in a highly
international and interdisciplinary research environment. <a
href="https://www.montpellier-france.com/"><font color="#1155cc"><u>Montpellier
is a dynamic Mediterranean city</u></font></a> with an
exceptional
environment, culture and quality of life. It is home to numerous
high-quality research institutes and the Montpellier University, a
vibrant 70,000 student population and one of the world’s oldest
medical schools.</p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
<u><b>The Lab: </b></u>The work will be carried out in the <a
href="https://www.igmm.cnrs.fr/team/ia-pour-linterpretation-du-genome/"><font
color="#1155cc"><u>AI
for Genome Interpretation (AI4GI</u></font></a>) group, led
by Dr.
Daniele Raimondi. The group focuses on the development of advanced
artificial intelligence and machine learning methods for genome
interpretation, with a particular emphasis on modeling the
relationship between genetic variation and phenotypic outcomes.</p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
AI4GI develops tailor-made neural network architectures, including
sparse and biologically informed models, to predict disease risk
and
complex quantitative traits from large-scale genomic data such as
whole-genome and exome sequencing. By combining methodological
innovation in AI with applications in human genetics, cancer
genomics, and plant genomics, AI4GI aims to advance our
understanding
of genotype–phenotype relationships, and precision medicine.</p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
<u><b>The project:</b></u> This project aims at developing a new
paradigm of General Genome Interpretation (GenGI) models by
combining
<b>DNA Large Language Models</b> (DLLMs) with <b>Deep Neural
Networks</b>
to predict human phenotypes directly from Whole Exome Sequencing
samples from the <b>UKBiobank</b>. The project aims at the
wide-spectrum prediction of human phenotypes, unlocking new
frontiers
in clinical genetics, precision medicine, disease risk prediction,
and Explainable AI on genomics data.</p>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
The candidate will:</p>
<ul>
<li>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0cm"> Start
by familiarizing with existing research and methods for genome
interpretation</p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0cm">Familiarize
with the sequencing data and its pre-processing</p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0cm">Study how DNA
LLM work, and develop solutions to integrate them into the
neural network architectures developed by the lab.</p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0cm">Focus on
developing <b>low level </b>solutions for the scalability of
neural networks and large language models to whole genome
sequencing data</p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0cm">Develop <b>from
scratch </b>algorithms and neural network architectures for
the prediction of structured outputs (i.e. trees, graphs)</p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0.42cm">Implement
and develop methods for the interpretation of neural network
predictions and outputs, including concept-based activation
and conterfactual analyses.</p>
</li>
</ul>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">The
project focuses on the development of new neural network
architectures to perform inference on sequencing data. </p>
<p class="western"
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
<strong>Candidate profile</strong></p>
<p class="western"><font size="2">Bioinformatics and genome
interpretation are
multidisciplinary and rapidly evolving fields. We are looking
for a
candidate who:</font></p>
<ul>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Has
a background in computer science, mathematics, or physics,
with a strong focus on machine learning</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Is
eager to continuously learn new skills, methods, and
concepts</font></p>
</li>
<li>
<p class="western"><font size="2">Enjoys tackling novel and
unforeseen challenges with strong problem-solving skills</font></p>
</li>
</ul>
<p class="western"><font size="2"><strong>Required skills and
expertise</strong></font></p>
<ul>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Strong
background in neural networks, machine learning, linear
algebra, and a working understanding of statistics</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Deep
understanding of machine learning foundations, including:</font></p>
<ul>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Linear
algebra (vector and matrix operations)</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Optimization
methods</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Neural
networks (with practical experience in PyTorch)</font></p>
</li>
</ul>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Solid
programming skills in Python and scientific computing (e.g.,
PyTorch, scikit-learn, NumPy)</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Proficiency
with GNU/Linux environments (including tools such as SSH)</font></p>
</li>
<li>
<p class="western"><font size="2">Good communication and
teamwork skills</font></p>
</li>
</ul>
<p class="western"><font size="2"><strong>Additional (preferred)
qualifications</strong></font></p>
<ul>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Familiarity
with GWAS, population genetics, or bioinformatics pipelines</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">Experience
processing genomic data (e.g., whole-exome or whole-genome
sequencing)</font></p>
</li>
<li>
<p class="western"><font size="2">Basic understanding of
genetics and biology</font></p>
</li>
</ul>
<p class="western"><font size="2"><strong>Other information</strong></font></p>
<ul>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">The
project involves developing unconventional neural network
models using PyTorch</font></p>
</li>
<li>
<p class="western" style="margin-bottom: 0cm"><font size="2">A
minimum English level of B2 is required</font></p>
</li>
<li>
<p class="western"><font size="2">Applications must be submitted
in English</font></p>
</li>
</ul>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
<font size="2"><b>Practical details</b></font></p>
<ul>
<li>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0cm"><font
size="2"> Location: IGMM, Montpellier</font></p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0cm"><font size="2">Duration:
<b>12 months</b>.</font></p>
</li>
<li>
<p style="line-height: 100%; margin-bottom: 0.42cm"><font
size="2">Starting date: flexible, but the candidate must be
selected <b>in the first half of 2026</b>.</font><br>
<br>
<br>
</p>
</li>
</ul>
<p
style="line-height: 100%; margin-top: 0.42cm; margin-bottom: 0.42cm">
If you’re interested in working at the crossroads of AI, machine
learning, bioinformatics and genomics - and in developing new
methods
rather than just applying existing ones - we’d like to hear from
you.</p>
<p style="margin-bottom: 0cm">Applications should be made at this
link: </p>
<p style="margin-bottom: 0cm"><a moz-do-not-send="true"
href="https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-107/Default.aspx"
class="moz-txt-link-freetext">https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-107/Default.aspx</a></p>
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