Universidade Federal de Santa Catarina

Position Overview: We are seeking an outstanding Postdoctoral Researcher in Artificial Intelligence (AI) and Data Science with expertise in multi-omics data integration for health and precision medicine. The successful candidate will join a multidisciplinary team developing AI-driven approaches to integrate and analyze genomics, transcriptomics, proteomics, metabolomics, and microbiome datasets to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data fusion, enabling next-generation solutions in precision health and biomedical research. Scientific Challenges Addressed in the Position: Heterogeneity and high dimensionality of multi-omics data requiring advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease progression modeling. Explainability and interpretability of AI models to support clinical decision-making and regulatory compliance in healthcare settings. Scalability and computational efficiency in processing and integrating massive multi-omics datasets from clinical cohorts. Key Responsibilities: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets and clinical metadata. Conduct network-based analysis (gene regulatory networks, protein-protein interaction networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with biologists, clinicians, and bioinformaticians for data interpretation and validation of computational findings in clinical or experimental settings. Disseminate research outcomes through publications in high-impact journals, conference presentations, and workshops. Mentor and support the training of graduate students and early-career researchers in AI and multi-omics integration. Required Qualifications: Ph.D. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or a related field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal modeling, and graph-based AI approaches. Experience in multi-modal data fusion, representation learning, and heterogeneous data integration. Strong publication record in relevant peer-reviewed journals. Excellent communication skills and ability to work in a multidisciplinary environment. Familiarity with cloud-based computing platforms (AWS, Azure, Google Cloud) and high-performance computing (HPC) environments. Understanding of data privacy, security, and ethical considerations in handling clinical data. Application Process: Interested candidates should submit the following documents in a single PDF: A cover letter outlining their research interests, motivation, and relevant experience. A detailed Curriculum Vitae (CV) with a list of publications. Contact details of two academic referees.

Job Description As part of our laboratory's research initiatives, we are conducting advanced research on the computational modeling and optimization of heterogeneous catalysts for various catalytic processes. This project focuses on utilizing computational techniques to understand and enhance the performance of catalysts in reactions such as hydrogenation, oxidation, and biomass conversion. We are seeking a highly motivated candidate to contribute to the development of new catalytic systems through computational simulations and theoretical studies. Main Responsibilities: Perform computational calculations to model and optimize heterogeneous catalysts. Conduct simulations to evaluate catalytic performance and reaction mechanisms. Analyze and interpret computational data to understand the interactions between catalysts and reactants. Write scientific reports and research articles and participate in conferences. Candidate Profile PhD degree in Chemical Engineering, Materials Science, or a related field. Strong knowledge of heterogeneous catalysis and computational chemistry. Experience with computational modeling  (i,e, DFT, and/or molecular dynamics). Familiarity with software such as Material Studio, VASP, Quantum Espresso… Excellent communication skills (oral and written) in English. Good publication track record in peer-reviewed journals. Ability to work independently and as part of a multidisciplinary and multicultural team. Experience in co-supervision and coordination of graduate students is desirable. Candidature Submission: Applicants should submit: Cover letter outlining research experience, and achievements, and stating research interests. Curriculum Vitae. List of publications. Name and contact information of at least two referees. Recommendation letter.

Department: Education Vacancy ID: 039964 Closing Date: 08-Mar-2026 The Role Maynooth University is committed to a strategy in which the primary University goals of excellent research and scholarship and outstanding education are interlinked and equally valued. The Education Department is seeking an excellent candidate to join our staff as a Research Assistant. This person will join the team working on a Research Ireland Discover award– an outreach project entitled “WILDCITY: Wildlife Inquiry & Learning for Discovery in Cities Involving Teens & Youth”. This project aims to build awareness and understanding regarding the scientific research process, biodiversity, and biodiversity conservation by engaging teachers, student teachers, and junior cycle students in gathering and analysing biodiversity data for school campuses and surroundings through individual, group-based, and school-wide initiatives, supported by the project team. The project fosters collaboration between researchers from the MU Education and Biology Departments and the School of Earth, Environment, and Sustainability at the University of Iowa, working closely with Green Schools Ireland. The appointed Research Assistant will contribute to project coordination, research activities, and engagement with participating schools. The role requires demonstrated experience in research and project management, along with the ability to work independently, think critically, and respond flexibly to emerging project needs. The successful candidate will be enthusiastic about working with student teachers and young people and will liaise with a range of stakeholders, including school staff and project partners. Experience in classroom teaching or educational settings is desirable. As part of the role, the Research Assistant will be expected to visit participating schools in Counties Dublin, Kildare, and Wicklow. The role will involve hybrid working arrangements, the candidate will be expected to carry out duties at MU campus, remotely, and during visits to partner schools. Salary Research Assistant (Level 1-5) (2026): €33,292 – €36,304 p. a. (1 point - with increment) Appointments will be made in accordance with public sector pay provisions. Closing Date: 23:30hrs (local Irish time) on Sunday 8th March, 2026. Please note all applications must be made via our Online Recruitment Portal at the following link: https://www.maynoothuniversity.ie/human-resources/vacancies Applications must be submitted by the closing date and time specified above. Any applications which are still in progress at the closing time on the specified closing date will be cancelled automatically by the system. Late applications will not be accepted. Maynooth University is an equal opportunities employer The position is subject to the Statutes of the University