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2026 The 10th International Conference on Computing and Data Analysishttps://www.iccda.orgInternational Conference on Computing and Data Analysis (ICCDA) is an annual conference held each year. It is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of computing and data analysis.ICCDA 2026 will be located in Phuket, Thailand during December 18-20, 2026. It is co-sponsored by Rajamangala University of Technology Srivijaya, Thailand and University of Thessaly, Greece.We believe the final program will be the result of a highly selective review process designed to include the best work of its kind in every category. You are cordially invited to submit your recent research work to the ICCDA 2026.Call for PapersTOPICS INTERESTED BUT NOT LIMITEDMathematical, probabilistic and statistical models and theoriesMachine learning theories, models and systemsKnowledge discovery theories, models and systemsManifold and metric learningDeep learningScalable analysis and learningHeterogeneous data/information integrationData pre-processing, sampling and reductionDimensionality reductionFeature selection, transformation and constructionLarge scale optimizationHigh performance computing for data analyticsArchitecture, management and process for data scienceData warehouses, cloud architecturesLarge-scale databasesInformation and knowledge retrieval, and semantic searchWeb/social/databases query and searchPersonalized search and recommendationHuman-machine interaction and interfacesCrowdsourcing and collective intelligenceLearning for streaming dataLearning for structured and relational dataLatent semantics and insight learningMining multi-source and mixed-source informationMixed-type and structure data analyticsCross-media data analyticsBig data visualization, modeling and analyticsMultimedia/stream/text/visual analyticsRelation, coupling, link and graph miningPersonalization analytics and learningWeb/online/social/network mining and learningStructure/group/community/network miningCloud computing and service data analysis

2026 8th International Conference on Big Data Management https://www.icbdm.org/index.htmlWelcome to ICBDM 2026! 2026 8th International Conference on Big Data Management (ICBDM 2026) will be held in Derby, UK during June 24-26, 2026! It's hosted by University of Derby. The theme of this year is "Data Management in Statistics and Data Science".The conference aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of big data management. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of big data management.Important DatesSubmission Deadline: April 5, 2026Acceptance Notification: May 5, 2026Registration Deadline: May 20, 2026Camera-Ready Version: May 20, 2026Early Bird Registration End: May 20, 2026JournalA few selected papers with extended version will be published in theJournal of Advanced Management ScienceISSN: 2810-9740 Frequency: SemiannuallyDOI: 10.18178/joamsAbstracting/ Indexing: CNKI, Google Scholar, CrossrefTemplate: JOAMS TemplateCall for PapersTopics of interest for submission include, but are not limited to:Track 1: Big Data Analysis and ManagementData Acquisition, Integration, Cleaning, and Best PracticesBig Data Search Architectures, Scalability and EfficiencyCloud/Grid/Stream Data Mining- Big Velocity DataSemantic-based Data Mining and Data Pre-processingBig Data as a ServiceData Lifecycle Management: From Collection to ArchivingData Governance Frameworks and Best PracticesData Management Standards (e.g., FAIR principles: Findable, Accessible, Interoperable, Reusable)Ethical Considerations in Data ManagementAlgorithms and Systems for Big Data SearchVisualization Analytics for Big DataChallenges in Managing Large-scale DatasetsBig Data Processing Frameworks (e.g., Apache Spark, Apache Flink)Scalable Storage Solutions for Big DataMobility and Big DataMethods for Data Collection: Surveys, Experiments, Sensors, Web ScrapingData Integration Techniques: ETL (Extract, Transform, Load) ProcessesSearch and Mining of Variety of Data including Scientific and Engineering, Social, Sensor/IoT/IoE, and Multimedia DataTrack 2: Data Structures and Data ModelsMultimedia and Multi-structured Data- Big Variety DataComputational Modeling and Data IntegrationRelational Databases (e.g., SQL) vs. NoSQL Databases (e.g., MongoDB, Cassandra)Data Warehousing and Data Lake ArchitecturesCloud-based Data Storage Solutions (e.g., AWS S3, Google BigQuery)Distributed Storage Systems for Big Data (e.g., Hadoop HDFS)Data Quality Metrics: Accuracy, Completeness, Consistency, and TimelinessTechniques for Data Cleaning and PreprocessingHandling Missing Data: Imputation Methods and StrategiesOutlier Detection and Treatment in DatasetsReal-Time Data Collection and Streaming Data ManagementImportance of Metadata in Data ManagementMetadata Standards and Schemas (E.G., Dublin Core, Schema.Org)Tools for Metadata Extraction and ManagementRole of Metadata in Data Discovery and ReuseVisualization of High-Dimensional DataManaging Unstructured Data (E.G., Text, Images, Videos)Data Silos and Interoperability IssuesTrack 3: Big Data Security and PrivacyVisualizing Large Scale Security DataThreat Detection using Big Data AnalyticsPrivacy Threats of Big DataPrivacy Preserving Big Data Collection/AnalyticsHCI Challenges for Big Data Security & PrivacySociological Aspects of Big Data PrivacyTrust Management in IoT and Other Big Data SystemsData Encryption and Anonymization TechniquesRole-based Access Control (RBAC) and Data PermissionsCompliance with Data Protection Regulations (e.g., GDPR, CCPA)Secure Data Sharing and Transfer ProtocolsVisualizing Large Scale Security DataBalancing Data Accessibility with SecurityTrust Management in IoT and Other Big Data SystemsHCI Challenges for Big Data Security & PrivacyTrack 4: Big Data Analysis Tools and Key TechnologiesHealthcare: Managing Electronic Health Records (EHR) and Patient DataFinance: Data Management for Fraud Detection and Risk AnalysisEnvironmental Science: Managing Climate and Satellite DataSocial Sciences: Handling Survey and Census DataE-Commerce: Customer Data Management and PersonalizationComplex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, TelecommunicationBig Data Analytics in Small Business Enterprises (SMEs)Big Data Analytics in Government, Public Sector and Society in GeneralReal-Life Case Studies of Value Creation through Big Data AnalyticsExperiences with Big Data Project DeploymentsBig Data as a ServiceBig Data Industry StandardsTrack 5: Application of Big Data in Information SystemsTools and Techniques for Exploratory Data Analysis (EDA)Interactive Dashboards for Data Exploration (E.G., Tableau, Power BI)Open-Source Data Management Tools (E.G., Apache Nifi, Talend)Data Management Platforms (E.G., Snowflake, Databricks)Cloud-Native Data Management SolutionsAutomation Tools for Data Pipelines (E.G., Airflow, Prefect)Data Pipelines for Machine Learning WorkflowsFeature Engineering and Dataset PreparationManaging Labeled and Unlabeled Data for Supervised and Unsupervised LearningData Versioning and Reproducibility in ML ExperimentsData Management for AI and Deep LearningBlockchain for Secure and Decentralized Data ManagementFederated Learning and Privacy-Preserving Data ManagementQuantum Computing and Its Impact on Data Management

PhD Call for Applications SummaryAcademic Year 2025/2026 – 41st CycleDeadline: 6 June 2025Research Field: Professions and applied sciencesRead more at https://euraxess.ec.europa.eu/jobs/funding/phd-2025/26-data-scienceApplicationThe Call for Applications for the 2025/2026 PhD Programs is officially open. Applicants, regardless of their nationality, age and gender are invited to submit their applications. Applicants can find all essential information in the relevant paragraphs of this guide.Further information along with possible modifications to, requirements, submission procedures, available positions and/or scholarships, will be given, before the deadline, through the pages:• https://dottorati.uniroma2.it/corsi-di-dottorato_p10297.aspx• https://dottorati.uniroma2.it/corsi-di-dottorato_p10299.aspx AboutOutlineThe availability of huge volumes of data, basically characterized by the increasingly extensive and pervasive use of digital technologies, leads the ongoing revolution in many areas of social, economic and industrial reality, posing new challenges to the scientific and technological research in Computer Science, Artificial Intelligence and in several other disciplines, from Physics to Economics, from Medicine to Human Sciences. In this context, the accessibility and processing of large amounts of data, both in centralized and distributed systems, their usage in the design and implementation of complex decision-making models, favors the study and development of autonomous systems in different fields (from mission-critical applications to the studies of natural and social phenomena, the prediction of economical dynamics as well as the diagnostics and planning in medicine or in industrial robotics). The PhD in Data Science is aimed, in its markedly interdisciplinary nature, to train, at the highest level, experts able to conduct research to understand and master the mathematical, statistical and computer science methodologies of data analysis and processing as well as the underlying technologies supporting applications in a wide variety of of scientific, industrial, economic, medical and social contexts. The reasons for a new PhD program in Data Science are many and significant. First of all, the offer in Central Italy of a doctoral training on these topics is still quite limited, but at the same time it is confronted with an increasing demand for experts in Data Science. This training should be understood with a characterization focused on mathematical-computer science skills, then a strong scientific-technological vocation, properly integrated on established statistical, economic, social and linguistic principles, thus able to give answers to the dynamics of this area of expertise that is very accelerated and strongly interdisciplinary and culturally heterogeneous. The School of Doctorate represents the indispensable ground for a wide range of students of Computer Science, Computer Engineering, Economics, Physics, Mathematics and not only, at our University of Tor Vergata. Today such students apply and succeed (according to various data sources) at other Schools. These students are attracted by initiatives in Data Science because in them is clear and dominant the role played by the computational knowledge, the strong scientific character of the educational objectives and the diversified potential applications: from Physics to Economics up to Social Sciences. It is interesting to note that many of the courses mentioned here, for example, the Master Degree Courses in Computer Science at the Department of Business Engineering, which have a large number of students, unlike other courses are not decreasing in number of enrolled students. This phenomenon is both cause and effect of the centrality that the themes of culture and digital transformation play in the industrial and social spheres of our country.See PHD IN DATA SCIENCE | TOR VERGATA

Hosting InformationOffer Deadline: Thu, 11 Sep 2025 - 23:59Organisation / Company: Monash University MalaysiaCountry: MalaysiaWebsitehttps://www.lancaster.ac.uk/health-and-medicine/https://www.monash.eduEmail- s.r.hall@lancaster.ac.uk- yap.michelle@monash.edu DescriptionBackgroundWe seek highly motivated postdoctoral researchers to submit expressions of interest (EOI) for a 3-year Marie Sklodowska-Curie Actions (MSCA) Postdoctoral Global Fellowship in drug development for snake venom toxins-induced necrosis. This prestigious fellowship offers an excellent opportunity for career development and international attachment in pharmacology and toxinology research. The fellowship also opens opportunities for international collaborations between Asia-Pacific and the EU. This fellowship also fosters early career development support. The candidate will lead innovative research projects in drug development for toxin envenomation, including mechanistic actions, structure-activity relationships, and preclinical assessment. Upon successful application, you will be hosted in Monash University Malaysia (Dr Michelle Yap – Toxin Pharmacology Lab) for 2 years and 1 year at Lancaster University (Dr Steve R Hall). Eligibility:Must hold a PhD in pharmacology, toxinology, biomedical science, biochemistry or biology-related field at the time of application. Meets MSCA requirements for this Global Fellowship: The applicant must be national or long-term resident (5 years+) of an EU Member State or Horizon Europe Associated Country. Demonstrates an excellent track record of publications. Demonstrates proficiency in research, laboratory, and data analysis skills; bioinformatics knowledge is an added advantage. Demonstrates proficiency in writing and communication skillsDemonstrates ability to work effectively in a team. To express interest, please submit a CV, and motivation to apply with two recommendation letters from academic supervisors or collaborators. We will contact the shortlisted candidate to prepare for the application. Closing Date for Applications:To allow ample preparation time for application and proposal writing, we anticipate the closing date to be 31st March 2025. However, please note that the call will remain open until a suitable candidate is found. Early submissions are encouraged.

Deadline: 22 May 2024Research Field: Professions and applied sciencesFunding Type: FundingMore Information: https://www.scientifyresearch.org/grant/career-development-award-basic-biological-science-italy/Project Key Words: Congenital heart disease, phonocardiogram, Artificial IntelligencePost summaryOur project focuses on harnessing the power of Artificial Intelligence (AI) to analyze phonocardiogram (PCG) data for the early detection of congenital heart disease (CHD). By developing advanced AI algorithms, we aim to revolutionize the way CHD is diagnosed and managed, leading to better outcomes for patients.Person specificationQualificationsEssentialHonours Degree (minimum 2:1) in biomedical engineering, computer science, electrical/ electronics engineering, or a related field.Strong background in signal processing and machine learning.DesirableProficiency in programming languages such as Python, R or MATLAB.Practical experience in simulation environments and Machine Learning libraries.Experience with cardiovascular physiology or cardiology research.Experience with GUI development and web applications. Knowledge & Experience EssentialExperience with machine learning techniques, including deep learning and neural networks.Strong analytical and problem solving skills. DesirablePrevious research experience in healthcare or medical signal processing projects.Experience with data visualization tools.Familiarity with phonocardiogram data analysis and interpretation. Skills & CompetenciesEssentialApplicants whose first language is not English must demonstrate on application that they meet SETU’s English language requirements and provide all necessary documentation. See Page 7 of the Code of PracticeIn order to be shortlisted for interview, you must meet the SETU English speaking requirements so please provide evidence in your application. DesirableExcellent written and verbal communication skills.Willingness and motivation to learn and experience new theoretical and technological areas.