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results. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop machine learning-based models from data.; - Validate the developed models with real data.; - Publicize the work in international
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of Machine Learning techniques. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based
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multidisciplinary team. Ability to write publications, reports, and projects. Oral and written communication skills. Computer skills/programming/modeling would be a plus. Candidate Criteria Ph.D. in Chemical
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 22 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
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projects on metabolic diseases * Develop and apply machine learning models for biomarker discovery, patient stratification, and prediction of disease trajectories * Collaborate with clinicians
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 22 days ago
Engineering Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Mechanical engineering Engineering » Computer engineering Researcher Profile First Stage
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data
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learning to model solid-state materials while collaborating with experimentalists. Qualifications • Ph.D. in Computational Physics/Materials Science, or related field (completed by start date
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 25 days ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine