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Permanent research engineer position as Head of Hub Algorithmics & AI pole
Publiée le 07/04/2025 16:24.
Référence : Permanent research engineer position as Head of Hub Algorithmics & AI pole.
CDI, Paris 15eme.
Entreprise/Organisme :Hub de bioinformatique et biostatistique de l'Institut Pasteur
Niveau d'études :Master
Durée du contrat :CDI
Secteur d'activité :bioinformatics, artificial intelligence, management
Description :The Hub of Bioinformatics and Biostatistics provides analytical support to research units and platforms at the Institut Pasteur. The Hub is committed to this mission through: Collaborating on scientific projects, submitted by research teams of our institute, to the Hub. Training scientific staff from the Institut Pasteur Paris or from other institutes of the international network of Instituts Pasteur. Developing tools and applications to be shared with the broader scientific community Interacting directly with scientist upon specific inquiries As head of the Algorithmics, AI, and Mathematical Modeling group, the recruited engineer will focus on applying and developing innovative AI solutions for genomics projects of the Institut Pasteur. He will oversee the group management and be accountable for its project portfolio. The recruited engineer will work with a team of computational biologists in a collaborative environment, interacting with other teams of the Hub, the Technology Department, the Computational Biology department, and the campus. As part of the Bioinformatics and Biostatistics Hub, the group lead will: Manage the group’s collaborative project portfolio Ensure the quality of work and scientific contributions of the engineers in the group Oversee administrative management and foster an open, collaborative work environment Support the professional development of team members Lead the methodological development in collaboration with the Computational Biology Department and the campus Represent the pole within the Technology Department and the campus As a member of the hub’s leadership team, the pole head will participate in the hub’s operational management and contribute to its strategy and implementation. Reporting: Reports to the Hub Leadership. Key Activities: Project planning and organization Project coordination, tracking, and resource management Thematic coordination of the group (methodological developments, best practices, etc.) Administrative management of the division Development of collaborators Workplace quality of life improvement Participation in hub leadership The group head role will represent approximately 50% of the workload. Additionally, the recruited candidate will act as a research engineer within the Hub, contributing to collaborative projects, teaching, and consulting in alignment with their leadership responsibilities. Profile: PhD/Master’s/Engineering degree in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, Biostatistics, or related fields At least 10 years of experience in a biomedical research institute and/or industry in computational biology, biostatistics, applied mathematics, or bioinformatics Proven expertise in deep learning, algorithmic approaches, and mathematical modeling applied to genomics Knowledge and experience in software development and best practices Strong leadership experience in group and/or project management in a complex organization; experience mentoring students Fluency in French and English, with experience working in multilingual environments Creativity and innovation Collaborative mindset, ability to manage complex and ambiguous situations Focus on professional development of team members To apply: Click on the following link and select the corresponding profile: https://hub-jobs2025.pasteur.cloud Please, submit your updated CV and a cover letter (motivation letter). You may indicate contact information for reference letters (3 max.). They will be automatically contacted when you validate your application.  We are a team committed to foster a fair, inclusive and diverse work environment. Diversity has been scientifically established as a key factor to improve scientific objectivity. Hence, all applicants will be evaluated solely based on qualification regardless of gender, gender identity, sexual orientation, race or disability.
En savoir plus :https://research.pasteur.fr/en/job/permanent-research-engineer-position-as-head-of-hub-algorithmics-
Contact :herve.menager@pasteur.fr
Physically interpretable AI emulator for hydrological extremes
Publiée le 03/04/2025 09:18.
Référence : PhD thesis, Montreal, Canada.
Thèse, Polytechnique Montréal, Canada.
Entreprise/Organisme :Polytechnique Montréal, Canada
Niveau d'études :Doctorat
Sujet :This PhD project offers a unique opportunity to contribute either to the advancement of deep learning methodologies or to hydrological impact studies, depending on the candidate's expertise and interests. The focus is on developing physically-coherent deep learning (DL) emulators that can downscale low-resolution climate projections to high-resolution outputs. These emulators will ensure physical consistency between key meteorological variables (e.g., precipitation, temperature) and improve their interpretability for practical applications. From a deep learning perspective, this project aims to address challenges in uncertainty quantification and the integration of physical constraints into DL emulators, offering the potential to work on cutting-edge techniques in AI applied to environmental systems. Alternatively, from a hydrological impact studies perspective, the project aims to assess climate change's impacts on small watersheds using emulated meteorological variables, with a particular focus on streamflow prediction and extreme events such as flooding. This interdisciplinary project has far-reaching implications for both fields, contributing to better climate adaptation strategies and enhanced hydrological risk assessments.
Secteur d'activité :AI for climate
Description :See above description of thesis project.
En savoir plus :https://www.polymtl.ca/expertises/en/physically-interpretable-ai-emulator-hydrological-extremes-carr
Contact :julie.carreau@polymtl.ca
Post-Doc in Machine Learning (Multiple Fairness in Recommending Systems)
Publiée le 05/02/2025 10:39.
Référence : Postdoc in Télécom Paris.
CDD, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau.
Entreprise/Organisme :Télécom Paris
Niveau d'études :Doctorat
Date de début :Printemps 2025
Durée du contrat :18 ou 36 mois
Secteur d'activité :Intelligence Artificielle
Description :Post-Doc in Machine Learning (Multiple Fairness in Recommending Systems) The group dedicated to Research in Machine Learning, Statistics & Signal Processing (the research group S2A) in Télécom Paris is recruiting a postdoc in Machine Learning (18 months contact, extendable to 36 months). The post-doc recruited will take part in an interdisciplinary collaborative research project involving the SES (Economics and Social Sciences) department of Télécom Paris and the Caisse des Dépôts et Consignations, a leading French public financial institution. Research assignment Research activities will focus on fairness issues for recommendation engines designed by means of machine-learning methods. With the explosion of digitized content available online, recommender systems have become an essential technology and a key element in the development of new services. In a commercial context, the algorithmic principles at work (e.g. collaborative filtering, user/content-based methods, hybrid approaches) in their operation are most often aimed exclusively at maximizing user satisfaction and increasing the platform's level of use. In the context of a public service, many other criteria and objectives must be integrated to ensure a fair service from the point of view of both users and suppliers (multi-sided fairness). It is precisely the subject of this collaborative project to propose and analyze (theoretically and empirically) methods for achieving acceptable trade-offs between the relevance of recommendations and bias mitigation. In addition to producing methodological research, the post-doc's mission will also include applied work on the current version of a deployed recommendation system, aimed at quantifying the presence of different types of bias resulting from its operation. Keywords: public service recommender system, fair and explainable AI, bias mitigation, multi-sided fairness Supervision: the recruit will work under the supervision of Sephan Clémençon (https://perso.telecom-paristech.fr/clemenco/) Winston Maxwell (https://www.telecom-paris.fr/winston-maxwell). Charlotte Laclau (https://laclauc.github.io/) Skills Education : PhD in Computer Science or in Applied Maths A short international postdoctoral experience is welcome but not mandatory English: fluent Expertise in Python programming, familiarity with database queries Capacity to work in a team and develop good relationships with colleagues in other disciplines Excellent writing and pedagogical skills Knowledge and experience required Research publications in Machine Learning (e.g. in Neurips, ICML, AISTATS, …) Knowledge of how recommending systems work Taste for AI applications and interest in its societal aspects Additional information The position does not involve teaching. However, on a voluntary basis, the postdoc recruited may take part in machine-learning courses (undergraduate/master level) coordinated by the supervisory team. The position 18 months position (extendable to 36 months) Télécom Paris, 9 place Marguerite Perey - 91120 Palaiseau - France Application Applicants should submit a single PDF file that includes: motivation letter curriculum vitae one or two major publications contact information for one or two references Important dates First-Quarter 2025: interviews with candidates (by visio-conference eventually) Spring 2025: beginning Contact for information/application Stephan Clémençon stephan.clemencon@telecom-paris.fr Charlotte Laclau charlotte.laclau@telecom-paris.fr Winston Maxwell winston.maxwell@telecom-paris.fr Related Websites https://s2a.telecom-paris.fr/ www.telecom-paris.fr/ai-ethics
En savoir plus :https://s2a.telecom-paris.fr/
Post-Doc in Machine Learning (Multiple Fairness in Recommending Systems).pdf
Contact :stephan.clemencon@telecom-paris.fr
PhD in modeling of soils
Publiée le 11/12/2024 11:14.
Référence : DeepHorizon.
Thèse, AgroParisTech , Palaiseau.
Entreprise/Organisme :UMR MIA, AgroParisTech
Niveau d'études :Master
Sujet :Developing a statistical spatial soil inference system with quantified uncertainty
Date de début :march 2025 or later
Durée du contrat :3 years
Rémunération :monthly gross salary ~ 2,100 €
Secteur d'activité :Interdisciplinary research in statistical machine learning and environmental sciences
Description :In the framework of the EU-project DeepHorizon (https://cordis.europa.eu/project/id/101156701), we are looking for an excellent PhD candidate to develop statistical methods supporting the development of a spatial soil inference system for European soils. A soil inference system uses known measurements, each with a certain level of uncertainty, to predict related soil properties with minimal error, by applying a series of logically connected (pedo)transfer functions (PTFs). The PhD candidate will start with an inventory of existing soil pedotransfer functions relevant to European soils and to calibrate usual mechanistic biogeochemical models. A large part of the work involves the exploration, development and application of new statistical approaches relevant for the inference system. The approaches should handle missing data along with uncertainty quantification of the input soil properties and propagation of the uncertainty throughout the inference engine. The candidate is expected to collaborate closely with other PhD candidates of the project consortium and with a project partner in Belgium, for which temporary stay could be envisioned.
En savoir plus :https://cordis.europa.eu/project/id/101156701
PhD_topic_PTFs.pdf
Contact :tabea.rebafka1@agroparistech.fr
Clustering de données fonctionnelles avec application en océanographie
Publiée le 01/10/2024 09:26.
Référence : Clustering de données fonctionnelles avec application en océanographie.
Thèse, Conservatoire National des Arts et Métiers, 2 rue Conté 75003 Paris.
Entreprise/Organisme :Conservatoire National des Arts et Métiers, Laboratoire CEDRIC
Niveau d'études :Master
Sujet :Classification non-supervisée pour l'identification de paysages acoustiques homogènes
Date de début :Entre fin 2024 et début 2025 en fonction de la date de recrutement du candidat
Durée du contrat :3 ans
Secteur d'activité :recherche
Description :Voir pièce jointe
En savoir plus :https://vincentaudigier.weebly.com/uploads/1/7/3/1/17317324/these_cnam_shom_clustering.pdf
these_cnam_shom_clustering.pdf
Contact :vincent.audigier@cnam.fr

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