Consulter les offres d’emploi

Junior statistical methodologist
Publiée le 23/07/2024 16:03.
Référence : Junior statistical methodologist.
CDI, Remote.
Entreprise/Organisme :Saryga
Niveau d'études :Doctorat
Secteur d'activité :Biostatistics
Description :As junior statistical methodologist, you will: • Provide statistical input and technical support on methods related to clinical trial designs, complex models, quantitative decision-making and/or biomarker research • Perform research work to develop new methodologies, write and / or participate to writing scientific publications • Collaborate with academia (supervision of students, cooperation with universities on research projects…) • Provide trainings to statisticians and non-statisticians You will have the opportunity to work on various therapeutic areas, often in complex settings with great value to the patients (rare diseases, innovative mechanisms of action…). The position is a permanent employment contract, full time, 100% remote in France (with some travels to attend meetings or conferences).
En savoir plus :https://saryga.com/careers/
2024-07_Saryga_Job_Statistical_methodologist_junior.pdf
Contact :gaelle.saint-hilary@saryga.com
Foundation Models for Physics-Aware Deep Learning
Publiée le 10/06/2024 11:50.
Référence : PhD position Sorbonne Université, Paris, Fr, Foundation Models for Physics-Aware Deep Learning.
Thèse, Paris.
Entreprise/Organisme :Sorbonne Universite
Niveau d'études :Master
Sujet :Abstract: Physics-aware deep learning aims at investigating the potential of AI methods to advance scientific research for the modeling of complex natural phenomena. This is a fast-growing research topic with the potential to boost scientific progress and to change the way we develop research in a whole range of scientific domains. An area where this idea raises high hopes is the modeling of complex dynamics characterizing natural phenomena occurring in domains as diverse as climate science, earth science, biology, fluid dynamics. Despite significant advances, this remains an emerging topic that raises several open problems in machine learning and application domains. Among all the exploratory research directions, the idea of developing foundation models for learning from multiple physics is emerging as one of the fundamental challenges in this field. This PhD proposal is aimed at exploring different aspects of this new challenging topic. Two main challenges will be investigated: learning from multiple physics and generalization with few shot learning.
Date de début :October/ November 2024
Durée du contrat :36 Months
Rémunération :2100 Euro Gross Salary
Secteur d'activité :Research - Artificial Intelligence
Description :Research Directions Foundation models have become prominent in domains like natural language processing (GPT, Llama, Mistral, etc) or vision (CLIP, DALL-E, Flamingo, etc). Trained with large quantities of data using self-supervision, they may be used or adapted for downstream tasks while benefiting through pre-training from large amounts of training data. Initial attempts at replicating this framework in scientific domains is currently being investigated in fields as diverse as protein (Jumper et al. 2021), molecule (Zhou 2023), weather forecasting (Pathak 2022, Nguyen 2023, Kochkov 2024). Is the paradigm of foundation models adaptable to more general physics modeling such as the complex behavior of dynamical systems? Large initiatives are emerging on this fundamental topic (https://iaifi.org/generative-ai-workshop). Some preliminary attempts are currently being developed (McCabe 2023, Subramanian 2023, Hao 2024). They suggest that learning from multiple steady-state or time dependent partial differential equations (PDEs) could enhance the prediction performance on individual equations. This high stake, high gain setting might be the next big move in the domain of data-driven PDE modeling. The objective of the PhD is to explore different directions pertaining to the topic of foundation models for physics, focused on the modeling of dynamical systems.
En savoir plus :https://pages.isir.upmc.fr/gallinari/open-positions/
2024-04-20-PhD-Description-Foundation-models-Physics.pdf
Contact :patrick.gallinari@sorbonne-universite.fr
Postdoc positions in applied statistics/applied econometrics for finance and meta-research
Publiée le 06/05/2024 15:58.
Référence : Postdoc positions in applied statistics/applied econometrics for finance and meta-research.
Postdoc, Liège (Belgium).
Entreprise/Organisme :University of Liège (Belgium)
Niveau d'études :Doctorat
Date de début :October 2024 (negotiable)
Durée du contrat :Up to 3 years
Rémunération :2850 EUR after tax
Description :CONTEXT Recent research identified the presence of biases in published empirical findings across the sciences, with economics and finance being no exceptions. Biases in published findings, due e.g. to p-hacking, pose a threat to the cumulative research process and to evidence-based decision making. In the framework of a WEAVE initiative jointly funded by Belgian research agencies FNRS and FWO, the inter-university project “Empirical analysis of p-hacking and advancement of methods to promote improved decision making” ambitions to address some of these issues over the period 2024 – 2028. In this project, we ambition to quantify, for the first time, the extent of these biases in the finance literature, and to identify its determinants, such as conflicts of interests. The project will conduct a large-scale meta-meta-analysis and aims at developing tailored anomaly detection methods to automatically identify these biases in research articles, leveraging recent findings in extreme value theory and distributional regression techniques. The selected postdoctoral researcher is expected to take a leading role in the empirical analyses conducted during the project, and to contribute significantly to the development of innovative statistical methods. The exact modalities of her/his involvement can be tailored to the interests of the candidate. It will be also encouraged that the candidate pursues some of his ongoing research efforts. This multidisciplinary project is conducted by the research group in Applied Statistics and Financial Econometrics at University of Liège - HEC Liège (Prof. J. Hambuckers), and the Environmental Economics Group at Hasselt University (Prof. S. Bruns). The postdoctoral researcher will be primarily supervised by Prof. Julien Hambuckers (https://sites.google.com/view/julienhambuckers/) and will work in close collaboration with Prof. Stephan Bruns (https://www.stephanbbruns.de/). (S)he will be a member of the following research centers: - the Financial Management for the Future group at ULiège – HEC Liège - the Environmental Economics Group at Hasselt University PROFILE Ph.D. in (applied) statistics, quantitative economics, econometrics, finance, or in applied science (with an empirical focus and an interest in finance or economics), or equivalent. Doctoral students close to completing their PhD degree are encouraged to apply. You have a strong quantitative background, ideally with a focus on (applied) statistics or econometrics or quantitative economics. • You have a strong research interest in some of the following topics: econometric methods, (applied) statistics, finance, economics and meta-analysis. • You have excellent problem-solving capacity. • You are cooperative and can work well in teams. • You have very good communicative skills. • You have a very good knowledge of English (French or German is considered a plus). • You will have resided in Belgium for less than 12 months over the previous 36 months at the beginning of the contract. • You are allowed to work in Europe. JOB OFFER • Internationally competitive environment supported by two research groups in two universities, ideal to develop a scientific network for a research career. • Up to 3 years of contract. • You will be appointed and paid as a postdoctoral researcher (ca. 2850 EUR /month after tax) • Financial support for conference participations and research stays in the framework of the project is provided. • Small teaching load (open to discussion). STARTING DATE, APPLICATION PROCEDURE AND QUESTIONS The expected starting date is October 2024 but negotiable for an earlier or a later date (max. starting date: December 2024). Applications are considered on a rolling basis. A regular physical presence at ULiege and UHasselt is expected, as well as participation to the everyday life of the departments (such as seminars and research events). Please send the application material (motivation letter, a two-page research statement and a CV with the name of two references) per email to Prof. Julien Hambuckers (jhambuckers@uliege.be). Questions may be sent to the same address.
En savoir plus :https://www.uliege.be/cms/c_8699436/en/uliege
Meta-research in finance - postdoc offer_ULIEGE.pdf
Contact :jhambuckers@uliege.be
Développement d’une méthode avancée de fusion de données en physico-chimie
Publiée le 09/04/2024 10:37.
Référence : Sujet de Post-doctorat.
Postdoc, Nancy, France.
Entreprise/Organisme :UMR 7360 CNRS Laboratoire Iinterdisciplinaire des Ecosystèmes Continentaux (LIEC)
Niveau d'études :Doctorat
Date de début :Dés que possible
Durée du contrat :12 mois (avec possibilité de renouvellement d'une année)
Rémunération :Environ 2130 euros bruts / mois
Secteur d'activité :Chimiométrie, Chimie Analytique, spectroscopies, data fusion
Description :Le sujet du post-doctorat s’inscrit dans le projet ICEEL Carnot Transverse intitulé TRANSFUSION pour Techniques de chimiométRie AvaNcéeS de FUsion de données pour repouSser les limites d’analyse d’appareils conventIONnels. L’objectif du projet TRANSFUSION est de développer des outils numériques innovants et performants permettant de repousser les limites instrumentales actuelles lors de la génération de données spatiales couplées à des informations de chimie élémentaire, moléculaire et mécanique. L’amélioration concernera à la fois la taille du plus petit objet observable, l’identification de sa composition ainsi que le temps de calcul. L’autre intérêt réside dans la transversalité disciplinaire des outils attendus, ceci à travers leur capacité à prendre en compte des environnements variables et des données de natures multiples et multi-échelle, applicables, dans la présente phase pilote, dans une large mesure aux domaines scientifiques des composantes ICEEL partenaires, à savoir : Génie des procédés et énergies (LERMAB), Ressources et environnement (LIEC, GeoRessources, et LCPME), Matériaux (LMOPS) et Technologies Industrielles (TJFU), où les plateformes instrumentales actuelles ont besoin de proposer une caractérisation plus fine et plus rapide des échantillons analysés pour leurs utilisateurs du publique, comme du privé.
En savoir plus :https://liec.univ-lorraine.fr/
Offre_postdoc_TRANSFUSION_2024_FR.pdf
Contact :marc.offroy@univ-lorraine.fr
POST-DOCTORAL position - Detection of neurovisual disorders on a driving simulator
Publiée le 31/01/2024 10:09.
Postdoc, 10-12 avenue de l'Europe 78140 Vélizy France.
Entreprise/Organisme :Laboratoire LISV / Université de Versailles Saint-Quentin (UVSQ)
Niveau d'études :Doctorat
Date de début :December 1st, 2024
Durée du contrat :10 month
Rémunération :3036€ gross/month
Secteur d'activité :Psychology, neuroscience, biomedical engineering or a related data scientist field
Description :General description A 10-month post-doctoral position is available at the University of Versailles Saint-Quentin (UVSQ) in the LISV laboratory (www.lisv.uvsq.fr) headed by Professor Eric Monacelli. The post is part of the APTICONDUITE research project, which is part of the 'Interactive Robotics' research team. Project description The APTICONDUITE project aims to develop a new methodology integrated into a driving simulator for assessing neurovisual disorders of drivers in handicap situation. This project combines a new multimodal experimental approach with advanced statistical modelling to provide a new theoretical and practical angle for studying neurovisual diseases in the field of adaptive driving. In this context, the post-doctoral student will initially contribute to analyzing data from a network of physiological sensors on board a driving simulator. He/she will have to retrieve relevant information to characterize the driving performance of a specific population (stroke, cognitive disorders, etc.). The second stage will involve developing a new methodology for the early detection of neurovisual disorders using artificial intelligence algorithms, in order to improve current clinical assessments. The data set will include images, electrical signals and indicators such as heart rate, breathing rate, etc... Qualifications We are looking for a PhD with experience in cognitive science and data processing: - PhD: the candidate could hold a PhD in psychology, neuroscience, biomedical engineering or a related data scientist field. With: - Advanced skills in data analysis and the use of statistical software. - Practical experience with artificial intelligence methods, including the use of machine learning algorithms to analyze complex data. - Knowledge of visual disorders and their implications for driving would be appreciated.
En savoir plus :www.lisv.uvsq.fr
Post-doc1_APTICONDUITE_ENGLISH.pdf
Contact :olivier.rabreau@uvsq.fr
Statisticien / Gestionnaire de bases de données
Publiée le 20/10/2023 09:12.
Référence : Statisticien / Gestionnaire de bases de données (INSERM).
CDD, Maternité Port-Royal, 123 bd Port-Royal, 75 014 Paris.
Entreprise/Organisme :INSERM
Niveau d'études :Master
Date de début :01/02/2024
Durée du contrat :12 mois, renouvelable
Rémunération :A partir de 2494,30 euros brut mensuels, selon l’expérience et le niveau de formation
Secteur d'activité :Santé
Description :EPOPé est une équipe de recherche mixte de l’Institut national de la santé et de la recherche médicale (INSERM) et de l’université Paris Cité, appartenant au Centre de Recherche Épidémiologie et Statistique (CRESS). Nos recherches portent sur la santé des femmes pendant la grossesse et ses suites, la santé des enfants liée au contexte de la naissance et la santé des enfants en pédiatrie courante, en France et au niveau international. Au sein de l’équipe EPOPé, le/la statisticien(ne) travaille sur les données de 2 projets de recherche : - il/elle exploite les données, définit et met en oeuvre des analyses statistiques qui visent à développer, valider et analyser des indicateurs de santé relatifs aux femmes enceintes et aux nouveau-nés à partir des bases de données médico-administratives du Système National des Données de Santé (SNDS) (temps dédié : mi-temps) ; - il/elle participe à l’harmonisation de ces indicateurs au sein d’un réseau européen.il/elle exploite la base de données du registre des malformations congénitales de Paris (remaPAR) qui enregistre de manière continue, depuis 1981, tous les cas d’anomalies congénitales détectées en prénatal ou durant la première semaine de vie parmi les naissances vivantes, les mort-nés et les interruptions médicales de grossesse. Les objectifs principaux sont d’assurer la surveillance épidémiologique des anomalies congénitales, d’évaluer les pratiques et les politiques de santé, et de contribuer à la recherche étiologique (temps dédié : mi-temps).
En savoir plus :https://rh.inserm.fr/nous-rejoindre/Lists/Emploi%20ITA/Attachments/4415/U1153%20-%20CRESS%20-%20Stat
U1153- StatisticienSNDSREMAPAR_2024.pdf
Contact :isabelle.monier@inserm.fr

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