Sort by
Refine Your Search
-
, algorithms with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5
-
systems where the candidate played an active role together with familiarity with deep learning methods. EVALUATION CRITERIA The selection will be based on the following criteria: CV: 50% Experience in
-
major research goals are highlighted: (i) the development of deep learning pipelines leveraging longitudinal user health data for knowledge extraction and medical decision support; and (ii
-
TEC. 2. OBJECTIVES: Collaborate with clinical partners in data collection and annotation Design and implement new deep learning solutions for the analysis of heart sound auscultation, electrocardiogram
-
benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
-
in the CV; b) Background and vocation for the design and development of large language models based on deep learning and capable of multimodal processing - information available in the cv and
-
-depth study of existing approaches to AI-based water leak detection and prediction, including classical methods and modern deep learning techniques. 2) Requirements survey Identification of the technical
-
plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep
-
Prof. João Pereira dos Santos, Assistant Professor 4. Fellowship Activities Plan: Portugal is currently facing a deep housing paradox: more than 723,000 dwellings were declared vacant in the 2021 Census
-
the deadline for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in