Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ICube UMR 7357
Durée : 6 mois
Contact : remi.allegre@unistra.fr
Date limite de publication : 2024-01-12

Contexte :
The aim of this internship is to identify and generate spatial arrangements of patterns relevant to the removal of material from solid materials for lightening purposes, in the context of construction and civil engineering applications.

Sujet :
The aim is to study geometric and statistical criteria for characterizing spatial arrangements of patterns in relation to desired mechanical properties, and to develop a generation algorithm.

A full description of the subject, together with information on how to apply, is available in the attached document.

Profil du candidat :
Skills:
– Some knowledge and experience in Computer Graphics and/or Image Processing is required.
– Experience in Machine Learning in Python is a plus.
– Curiosity and ability to communicate and share your progress and to make written reports.
– Autonomy and good organization skills.

Formation et compétences requises :
Academic level equivalent to a Master 2 in progress or Engineer in its 5th year, in Computer Science with courses in Computer Graphics and/or Image Processing, and Machine Learning.

Adresse d’emploi :
ICube – UMR 7357
300 boulevard Sébastien Brant
67400 ILLKIRCH

Document attaché : 202311221751_2023-2024-ASAM_Stage_M2_EN.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIMOS at Institut Henri Fayol, Mines Saint-Étienne
Durée : 5 to 6 months
Contact : antoine.zimmermann@emse.fr
Date limite de publication : 2024-04-01

Contexte :
Physical quantities form an important part of what is represented in scientific data, medical data, industry data, open data, and to some extent, various private data.

Whether it is distances, speeds, payloads in transportation, concentrations, masses, moles in chemistry, powers, intensities, voltages in the energy sector, dimensions of furniture, weights, heights of people, durations, and many others in health, there is a need to represent physical quantities, to store them, to process them, and to exchange them between information systems, potentially on a global scale, often on the Internet and via the Web.

Sujet :
In this internship, we seek to precisely define a way to unambiguously represent physical quantities for the Web of Data. More precisely, we will study the proposals made to encode physical quantities in the standard data model of the Semantic Web, RDF. We will be particularly interested in the use of a data type dedicated to this encoding, probably adapted from the proposal of Lefrançois & Zimmermann (2018) based on the UCUM standard.

Having established a rigorous definition of the data type (possibly its variants, if relevant), we will focus on implementing a module that can read/write and process physical quantities and their operations within the RDF data manipulation APIs, for the management, querying and reasoning with knowledge graphs containing physical quantities.

The ambition is that, on the one hand, the specification will become in a few years a de facto standard, before perhaps becoming a de jure standard; and that, on the other hand, the implementation will be the reference allowing to compare the compliance levels of other future implementations.

This study should lead to the publication of a scientific paper in a high impact scientific journal.

References
1. Maxime Lefrançois and Antoine Zimmermann (2018). The Unified Code for Units of Measure in RDF: cdt:ucum and other UCUM Datatypes. In The Semantic Web: ESWC 2018 Satellite Events – ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers, volume 11155 of the Lecture Notes in Computer Science, pp196–201, Springer.
2. Gunther Shadow and Clement J. McDonald. The Unified Code for Units of Measure. Technical report, Regenstrief Institute, Inc, November 21 2017.

Complete description available at https://www.emse.fr/~zimmermann/Teaching/SemWeb/Internship/

Profil du candidat :
Interested in the definition of specifications and their implementation.
Interested in research activities.

Formation et compétences requises :
Master 2 in computer science
Knowledge of Semantic Web technologies
Java programming
Preferably good writing skills

Adresse d’emploi :
École des mines de Saint-Étienne, bâtiment espace Fauriel, 29 rue Ponchardier, Saint-Étienne. https://www.openstreetmap.org/node/2794933485

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LISTIC – Laboratoire d’Informatique, Systèmes, Tr
Durée : 4-5 mois
Contact : faiza.loukil@univ-smb.fr
Date limite de publication : 2024-04-01

Contexte :
Depuis plusieurs années, des travaux sont menés au LISTIC expérimentant des méthodes d’analyse de données et proposant des outils (applications mobiles) dans l’objectif d’estimer le risque d’avalanche lors de sorties en ski alpinisme. Une sous partie de ces travaux concerne l’analyse de données météorologiques
précipitations, vent) avec pour objectif l’identification et l’étude de corrélations entre des phénomènes météorologiques, la situation géographique du lieu (massif montagneux) et le déclenchement d’avalanches.

Sujet :
Objectif du stage :
L’objectif du stage est de poursuivre ces travaux en utilisant des méthodes d’analyse de données et d’IA. En partant des données de précipitation de certaines stations météo dans les massifs alpins savoyards, des données temporelles (jour, heure) et de l’historique des avalanches disponibles, peut-on déterminer (prédire) les conditions (météo) qu’il va y avoir sur d’autres stations dans un certain périmètre géographique ?
Dans un second temps, le travail portera sur la détermination d’un seuil pluviométrique à partir duquel les avalanches sont davantage susceptibles de se produire ; actuellement, le seuil est déterminé par l’expert qui s’en remet à son expérience.
L’étude porte sur des données issues de différentes sources (stations météo, sites Web fournissant des données météo et données sur les avalanches).
Le travail consiste :
– à identifier et compléter les différentes sources de données qu’il est possible d’utiliser
– à mettre en place un processus systématique d’analyse de données (allant de la préparation des données à leur analyse)
– à sélectionner les méthodes d’analyse qui sont pertinentes et permettraient de “prédire” l’apparition de conditions météo (pluviométriques) favorables à de potentiels déclenchements
d’avalanches
– à proposer une chaîne automatisée de traitements, générique
et reproductible.

Profil du candidat :
Etudiant-e- en M2 ou 5ᵉ année École Ingénieur en Informatique.

Formation et compétences requises :
Compétences requises :
Méthodes d’analyse de données et IA, Apprentissage Machine,
Programmation Python. Un intérêt pour les activités de montage est attendu chez le/la candidat-e.

Adresse d’emploi :
LISTIC – POLYTECH Annecy-Chambéry
5 chemin de Bellevue – Annecy-le-Vieux, France.

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LITIS Lab (Rouen)
Durée : 5 to 6 months
Contact : paul.honeine@univ-rouen.fr
Date limite de publication : 2024-04-01

Contexte :
Safe and trustworthy Artificial Intelligence (AI) is central in the deployment of any AI system in major application areas, such as medicine and autonomous vehicles. Its major keystone requirements in Machine Learning (ML) have been recently investigated by researchers of the ML group in the LITIS Lab, including robustness, explainability and fairness. The current internship aims to address anomaly detection, which is a major ingredient of robust ML for Safe and trustworthy AI.

Sujet :
Self-supervised learning has recently emerged as a novel paradigm in Machine Learning, aiming to learn deep representations from unlabeled data. Its main driving force is contrastive self-supervised learning. A main ingredient in contrastive learning is a training scheme that contrasts each sample with augmented versions of itself, where augmentation strategies in imagery include color jittering, image rotation, image flipping and affine geometric transformations. Contrastive learning has been largely investigated for classification tasks, often demonstrating its relevance on well-known image classification benchmarks. However, such classification tasks with labelled training data do not get the most out of the self-supervised learning paradigm.

The goal of this internship is to explore contrastive learning for out-of-distribution detection in time series data. This would allow to take full advantage of the self-supervised learning paradigm for out-of-distribution detection (also called anomaly or novelty detection). The tasks to be carried out by the intern are as follows: The intern will implement different contrastive learning models. She/he will study augmentation methods that are relevant for time series, either by revisiting image transformations in the light of time series or by using distribution-shifting augmentations. The intern will conduct experiments on real time series by considering two contexts: detection from a batch of time series data, and online detection, namely in the context of streaming data.

This internship may lead to a PhD thesis.

Research Environment: This intern will conduct her/his research within the Machine Learning group in the LITIS Lab, under the supervision of Prof. Paul Honeine. This internship will be conducted within a research project gathering 9 permanent researchers of the LITIS Lab and the intern will also interact with several PhD students and interns also working on deep anomaly detection for time series.

References

– Hendrycks, Dan, Mantas Mazeika, Saurav Kadavath, and Dawn Song. “Using self-supervised learning can improve model robustness and uncertainty.” Advances in neural information processing systems 32 (2019).
– Li, Chun-Liang, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. “Cutpaste: Self-supervised learning for anomaly detection and localization.” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9664-9674. 2021.
– Liu, Xiao, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. “Self-supervised learning: Generative or contrastive.” IEEE transactions on knowledge and data engineering 35, no. 1 (2021): 857-876.
– Tack, Jihoon, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin. “CSI: Novelty detection via contrastive learning on distributionally shifted instances.” Advances in neural information processing systems 33 (2020): 11839-11852.

Profil du candidat :
Student in final year of Master or Engineering School, in data science, artificial intelligence, applied mathematics, or related fields.

Formation et compétences requises :
– Strong skills in advanced statistics and Machine Learning, including Deep Learning
– Good programming experience in Python

Adresse d’emploi :
LITIS Lab, University of Rouen Normandy, Saint Etienne du Rouvray (Rouen, France).

Application: Applicants are invited to send their CV and grade transcripts by email to paul.honeine@univ-rouen.fr.

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LITIS Lab, Rouen
Durée : 5 to 6 months
Contact : paul.honeine@univ-rouen.fr
Date limite de publication : 2024-04-01

Contexte :
Safe and trustworthy Artificial Intelligence (AI) is central in the deployment of any AI system in major application areas, such as medicine and autonomous vehicles. Its major keystone requirements in Machine Learning (ML) have been recently investigated by researchers of the ML group in the LITIS Lab, including robustness, explainability and fairness. The current internship aims to address anomaly detection with explainable models/results, which is a major ingredient of robust ML for Safe and trustworthy AI.

Sujet :
The broad interest in deep neural networks has driven recent advances in anomaly detection, also called out-of-distribution or novelty detection. Deep anomaly detection methods fall within three major categories: Deep one-class, variational autoencoders (VAEs) and generative adversarial networks (GANs) [1, 2]. While VAEs and GANs do not allow an exact evaluation of the probability density of new samples, they also suffer from notorious training instability (mode collapse, posterior collapse, vanishing gradients and non-convergence), as corroborated by many research studies [3]. For these reasons, we will investigate Normalizing Flows (NF), an emerging class of generative models where both sampling and density evaluation are efficient and exact, and where the latent representation is learned through an invertible transformation [4]. NF provide explainable models, are interconnected with Optimal Transport and have solid foundations for probabilistic modeling and statistical inference [5].

The goal of this internship is to explore Normalizing Flows for anomaly detection on time series. While NF have been previously explored with success for anomaly detection in images, they were seldom investigated for time series. The tasks to be carried out by the intern are as follows: The intern will first study relevant work on NF for anomaly detection, and then revisit them in the light of time series. She/he will explore two contexts: detection from a batch of time series data, and online detection on streaming data. For the latter, a particular attention will be paid to sequential detection. The intern will implement the different NF-based models and conduct experiments on real time series.

This internship may lead to a PhD thesis.

Research Environment: The intern will conduct her/his research within the Machine Learning group in the LITIS Lab, under the supervision of Prof. Paul Honeine. This internship is within a research project gathering 9 permanent researchers of the LITIS Lab and the intern will also interact with several PhD students and interns also working on deep anomaly detection for time series.

References

[1] L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller, “A Unifying Review of Deep and Shallow Anomaly Detection,” Proceedings of the IEEE, vol. 109, no. 5, pp. 756–795, 2021.
[2] G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, “Deep learning for anomaly detection: A review,” ACM Computing Surveys, vol. 54, no. 2, pp. 1–38, 2021.
[3] D. Saxena and J. Cao, “Generative adversarial networks (GANs) challenges, solutions, and future directions,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–42, 2021.
[4] I. Kobyzev, S. J. Prince, and M. A. Brubaker, “Normalizing Flows: An Introduction and Review of Current Methods,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 43, no. 11, pp. 3964–3979, 2021.
[5] G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing Flows for Probabilistic Modeling and Inference,” Journal of Machine Learning Research, vol. 22, no. 57, pp. 1–64, 2021.

Profil du candidat :
– Student in final year of Master or Engineering School, in data science, artificial intelligence, applied mathematics, or related fields.

Formation et compétences requises :
– Strong skills in advanced statistics and Machine Learning, including Deep Learning
– Good programming experience in Python

Adresse d’emploi :
LITIS Lab, University of Rouen Normandy, Saint Etienne du Rouvray (Rouen, France).

Applicants are invited to send their CV and grade transcripts by email to paul.honeine@univ-rouen.fr.

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : IRISA/UBS
Durée : 6 mois
Contact : chloe.friguet@irisa.fr
Date limite de publication : 2024-04-01

Contexte :
Detailed topic at: http://www-obelix.irisa.fr/files/2023/11/2024_IRISA-UBS_internship_Active-learning-and-object-detection.pdf

The context of this internship is motivated by issues raised in studies
with data collected by airborne imagery. The automation of the processing of this data, by
object detection methods and supervised learning, requires annotated databases. The annotation
step is therefore a task of great interest, both in machine learning (ML) and computer vision
(CV). Carrying it out manually is tedious and costly in terms of time and human resources.
Furthermore, in the case of multimodal images (i.e. acquired by several sensors), annotation
must be performed for each modality.
Active Learning (AL) is related to semi-supervised Machine Learning in which a learning
algorithm can interact at each iteration with the user to get some information about labels of
new data during the training step. It is motivated by situations in which it is easy to collect
unlabeled data but costly (time, money, tedious task) to (manually) obtain their labels. It stems
from the idea that we should only acquire labels that actually improve the ability of the model
to make accurate predictions. Instances that are more useful than others according to some
performance measures have to be identified to create an optimal training dataset: well chosen,
fewer representative instances are needed to achieve similar performance as if we label and use
all available data. This selection process has been investigated as selective sampling [9]. The
importance of an instance is related to a high level of both the information and uncertainty
relative to the trained model, considering therefore a trade-off between informativeness (ability
to reduce the uncertainty of a statistical model) and representativeness (ability to represent the
whole input data space) of the selection process [6].
In remote sensing, AL has therefore become an important approach to collect informative
data for object detection and supervised classification tasks, and to assist the annotation process.
The effectiveness of object detection models is intricately tied to the quantity of annotated data
at their disposal. To overcome this challenge, AL attempts to formulate a strategy for cherrypicking pertinent data that an annotator should annotate, as elucidated by Choi et al. [5]. This
typically involves employing a scoring mechanism that is related to the model’s uncertainties
about the data. Computationally, ascertaining these uncertainties usually necessitates a multimodel approach. However, it’s noteworthy that these ensemble techniques are resource-intensive.
Hence, the overarching objective of AL lies in the formulation of a classification function that
faithfully mirrors the data’s contribution to the learning process.

Sujet :
In the paper by Brust et al. [3], a novel approach to object detection using
deep learning is introduced. Their approach incorporates AL strategies to explore unlabeled
data. The authors proposed and compared various learning metrics that are suitable for most
object detectors, taking into account class imbalance.
To start this project, the first step involves evaluating the performance of a multimodal
object detector (like YOLOrs [10], SuperYOLO [13], YOLOFusion [7] …) with respect to these
1
metrics by applying them to a single modality (RGB for example). This evaluation will be
carried out under different settings, including various sizes of the initial dataset and different
adjustments of algorithm parameters. Then, the aim is to extend the AL strategy to the case
of multimodal images. Indeed, for each object all modalities do not contribute equally to the
classification/localization tasks, one can be more informative than the other.
Finally, metrics proposed by Brust et al. [3], focus on classification uncertainty, however,
the aspect of localization is overlooked. To get the uncertainty of localization, we can use a
strategy like the one of the Gaussian YOLO approach [4, 5] that provides both classification
and localization uncertainties which we can then use with Brust et al. metrics.

Profil du candidat :
Student in computer science and/or machine learning and/or signal & image processing and/or applied statistics

Formation et compétences requises :
good programming skills in Python (Pytorch knowledge appreciated), knowledge of deep-learning for image analysis, and high interest to investigate machine learning methods.

Adresse d’emploi :
IRISA, UBS, Campus de Tohannic, 56000 Vannes

Document attaché : 202311201649_2024_IRISA-UBS_internship_Active learning and object detection.pdf

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : IRISA/UBS
Durée : 6 mois
Contact : minh-tan.pham@irisa.fr
Date limite de publication : 2024-04-01

Contexte :
Detailed topic at: http://www-obelix.irisa.fr/files/2023/11/2023_master_topic_MTL.pdf

In recent years, deep neural networks have been successfully adopted in almost every application
domains of computer vision, including remote sensing for earth observation. The vast number
of remote sensing images captured from frequent satellite passes or aerial acquisition, however,
are not readily usable to train deep networks developed for generic vision problems due to the
lack of task-specific annotations and possible domain gaps.
On the other hand, the individual development efforts of various research groups for their
particular problems result in cluttered annotations and modalities: each dataset is typically
annotated for a few tasks while many tasks may be related to one another and could be jointly
learned to leverage complementary information and improve their performance. Coupling solving
different but related tasks, or well-known in the ML community as multi-task learning, has also
gained increasing attention in the remote sensing community. As multi-task learning aims to
predict different targets from the same inputs, it typically requires annotations of all the target
tasks for each input example to learn the interrelationship at the shared encoder by optimizing
all tasks at the same time.
Obtaining extra annotations to maintain multi-task datasets, however, add extra burden
to the development process. Recently, it has been shown in the vision community that that
multi-task learning could be beneficial even when the tasks are partially annotated [2]. Training
a network for multiple task while the training examples are annotated for a single task can
improve the performance of both tasks. Such discovery could be of interest to explore for the
benefit of remote sensing community.

Sujet :
This project is aimed to research the combination of different datasets annotated for different
tasks which may follow different statistical distributions to benefit and improve performance of
one another. To that end, we will focus on the object detection, i.e. bounding boxes prediction,
and semantic segmentation tasks, which are closely related yet not trivial to combine due to differences in spatial structure and information granularity: object detection predicts bounding-box
coordinates at object instance level while semantic segmentation provides per-pixel predictions
of category including amorphous regions. A general scheme is shown in Figure 1. Another
challenge of the project is to bridge possible domain gaps between the participating datasets
with possible approaches including generative models (GANs, diffusion models, etc.)

Profil du candidat :
Student in computer science and/or machine learning and/or signal & image processing;

Formation et compétences requises :
Python programming and familiarity with deep learning framework (Pytorch/Tensorflow);

Adresse d’emploi :
IRISA (UMR 6074) is located in the UBS (Université Bretagne Sud), campus de Tohannic, Vannes 56000, France.

Document attaché : 202311201646_2023_master_topic_MTL.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : IBISC. Université Paris Saclay, Univ Evry
Durée : 6 mois
Contact : fariza.tahi@univ-evry.fr
Date limite de publication : 2023-12-31

Contexte :
Determining the 3D structure of ribonucleic acid (RNA) chains is essential to understanding their function and role in the various stages of living organisms and viruses. Due to the high cost of experimental methods (NMR, cristallography, etc.), computational methods could be very helpful. Although methods have been proposed in the literature for several years, the task remains open. For proteins, this problem has witnessed tremendous advances in recent years: DeepMind’s AlphaFold2 [1] made a giant leap in solving the 3D structure prediction problem for many types of single-chain protein structures using deep learning. Unfortunately, RNA still remains challenging [2]: unlike for proteins, (i) data of known 3D RNA structures are not available in large quantities; (ii) RNA are not stable and thus may have different 3D conformations; (iii) RNA sequences can vary from a few nucleotides to several tens of thousands of nucleotides.
It is suggested in the literature that the only way to address these challenges is for the quantity of RNA structures or sequence alignments to catch up with the amount of protein data that is currently accessible for models like AlphaFold [2]. We think the solution does not solely lie in the quantity of data but in finding suitable search biases and principled ways to incorporate domain knowledge into the learning process. The metalearning paradigm can provide answers to these challenges. This paradigm aims to improve a learning model’s generalization capabilities by leveraging prior knowledge from a family of tasks and accumulating past experience in a meaningful way [5, 6, 3]. Gradient-based metalearning approaches are examples of this paradigm, where the goal is to learn a model that knows how to adapt to new tasks or domains using limited quantities of data [5, 13, 4]. These approaches made tremendous breakthroughs in many applications where adaptation to new tasks required only a few learning examples.

Sujet :
Recently, only a very few studies in the literature [7, 8, 9, 10] have started to address the use of metalearning in computational biology. Works like [7] and [8] have been limited to the prediction of non-coding RNA using metalearning, leaving the structural level unexplored. In this internship, we want to investigate metalearning for the problem of 3D structure prediction of RNA chains. In particular, the ability to leverage the 3D conformations coming from multiple known species and know how to adapt rapidly to new ones under few available samples.
Furthermore, we want to investigate how prior knowledge can be leveraged to guide the adaptation process further [6]. For example, we can exploit the knowledge base of prominent RNA structural patterns provided in the CaRNAval dataset maintained by the LISN laboratory at UPSaclay [11]. Concretely, the prior knowledge in the form of RNA structural patterns can, for example, be used to devise better parameterizations for the optimization landscapes induced by the initial learning problem.
We will use the RNANet [12] database, developed by our research team, that integrates various information on RNA, including sequences, families (e.g., MSA multiple sequence alignments), secondary structures, 3D structures, etc.
The steps of the internship will, first, consist of the study of the state-of-the-art on RNA 3D structure prediction and gradient-based metalearning approaches. Second, frame the problem of RNA 3D prediction in the metalearning setting and build a first metalearning-based architecture for RNA 3D structure prediction. Third, study the prominent RNA structural patterns included in the CaRNAval knowledge base and propose a way to leverage such structural patterns to devise better parameterizations for the learning process. Finally, benchmark with RNANet dataset and possibly other datasets.
From a methodological point of view, we want to develop new metalearning approaches that can effectively deal with limited data in predicting the 3D structure of RNA and incorporate prior knowledge into the learning process.
From a bioinformatic perspective, we would like to propose an efficient tool for predicting RNA 3D structures, a tool that could be used in a personalized medicine project we are involved in, the IHU Prometheus project (2024-2034) on Sepsis, where RNAs can be potential biomarkers and/or therapeutic targets.
The developed tool will be available through our EvryRNA bioinformatics platform (http://evryrna.ibisc.univ-evry.fr), a platform providing the scientific community with several tools developed under the team for the analysis and prediction of non-coding RNAs.
The internship can lead to a Ph.D. thesis to further deepen the use of metalearning for the prediction of RNA 3D structures.
References
[1] Jumper, John, et al. “Highly accurate protein structure prediction with AlphaFold.” Nature 596.7873 (2021): 583-589.
[2] Schneider, Bohdan, et al. “When will RNA get its AlphaFold moment?.” Nucleic Acids Research 51.18 (2023): 9522-9532.
[3] Hospedales, Timothy, et al. “Meta-learning in neural networks: A survey.” IEEE transactions on pattern analysis and machine intelligence 44.9 (2021): 5149-5169.
[4] Nichol, Alex, and John Schulman. “Reptile: a scalable metalearning algorithm.” arXiv preprint arXiv:1803.02999 2.3 (2018): 4.
[5] Finn, Chelsea, Pieter Abbeel, and Sergey Levine. “Model-agnostic meta-learning for fast adaptation of deep networks.” International conference on machine learning. PMLR, 2017.
[6] Hamidi, Massinissa. Metalearning guided by domain knowledge. Diss. Université Paris-Nord-Paris XIII, 2022.
[7] Li, Zhongshen, et al. “CoraL: interpretable contrastive meta-learning for the prediction of cancer- associated ncRNA-encoded small peptides.” Briefings in Bioinformatics 24.6 (2023): bbad352.
[8] Bonidia, Robson P., et al. “BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.” Briefings in Bioinformatics 23.4 (2022): bbac218.
[9] Wu, Xue, et al. “Meta-learning shows great potential in plant disease recognition under few available samples.” The Plant Journal (2023).
[10] Rodrigues, Vânia, and Sérgio Deusdado. “Metalearning approach for leukemia informative genes prioritization.” Journal of Integrative Bioinformatics 17.1 (2020): 20190069.
[11] Reinharz, Vladimir, et al. “Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.” Nucleic acids research 46.8 (2018): 3841-3851.
[12] Becquey, Louis, Eric Angel, and Fariza Tahi. “RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures.” Bioinformatics 37.9 (2021): 1218-1224.
[13] Raghu, Aniruddh, et al. “Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML.” International Conference on Learning Representations. 2019.

Profil du candidat :
Master 2 (or equivalent) in DataSciences, Computer Sciences or bioinformatics / Computational Biology

Formation et compétences requises :
Master 2 (or equivalent) in DataSciences, Computer Sciences or bioinformatics / Computational Biology

Adresse d’emploi :
IBGBI building. 23 bv. de France, Evry

Document attaché : 202311191845_Internship-Metalarning-RNA3D.pdf