Post-doctoral position in dissimilarity learning for interactive clustering
il y a 2 jours
clusterings.Offre d'emploi (Non permanent)Post-doctoral position in dissimilarity learning for interactive clusteringPrésentationThe research performed in SCHISM will focus on interactive pattern-mining,and interactive clustering in a chemoinformatics context. Interactive data-mining is a recent research direction that breaks with the older paradigm ofspecifying parameter settings for algorithms, letting the algorithm run, interpretthe results of the operation, and, based on this interpretation, adjust parame-ter settings to restart the process. Interactive data-mining, on the other hand,proposes partial or preliminary results to the user, collects their feedback, anduses this feedback to bias the mining process going forward.The overall goal of SCHISM is to develop a robust approach to interactivedata-mining that integrates both pattern-mining and clustering, and to delivera prototype that allows users to launch pattern mining or clustering algorithms,visualize the results, give feedback, and rerun mining operations, which will takethe given feedback into account.MissionThe general idea of the research work carried out at LITIS lab for the SCHISMproject is to rely on dissimilarity representations to both infer relevant cluster-ings and to propose tools for visualization, analysis and expert feedback underthe form of constraints on the clusters. The goal is to de_ne a common repre-sentation so that the data/constraints are projected into smaller spaces whilepreserving the properties of the neighborhood. To do so, a preliminary studyhas been carried out the last few months on using Random Forest (RF) mod-els to measure dissimilarities and on using it afterwards for inferring relevantclusterings.The post-doctoral researcher will be in charge of (i) deepening the use of RFdissimilarities for deriving a relevant clustering and (ii) proposing mechanisms ofanalysis and interaction with the expert. More speci_cally, this latter task mustmeet a two-fold objective, namely allowing the expert to select and analyze(sub-)clusterings in order to propose a feedback, and translating this feedback intothe form of dissimilarity constraints in order to adjust the result. This researchwork will therefore involve becoming familiar with Random Forest methods andinteractive clustering.Profile du candidatThe successful applicant will:possess or be on track to complete a PhD in computer science, or appliedmathematics with a focus on machine learning or data-mining, Fluency inwritten and spoken English or French is essential;have strong programming skills (Java, Python, etc.) and in-depth under-standing of statistics and machine learning;have a productive publication record;have a strong work ethic and time management skills along with the abilityto work independently and within a multidisciplinary team as required.Salary will be in line with European and French guidelines w.r.t. years ofresearch experience. Additional funding is available for travel.Compétences requisesOrganisationDate candidature : dès maintenantContact :Sent to Laurent Heutte (laurent.heutte@univ-rouen.fr) and Simon Bernard (simon.bernard@univ-rouen.fr)Documents requis pour postuler:curriculum vitaestatement of past research accomplishments, career goal and how thisposition will help you achieve your goalstwo representative publicationscontact information for three referencesDébut: asapDurée: 24 moisSalaire :Localisation: Université de Rouen, UFR Sciences, Site du MadrilletDocument: détail de l'offre (pdf) #J-18808-Ljbffr
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Postdoc: Dissimilarity Learning for Interactive Clustering
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