10 machine-learning-modeling-"https:"-"Computer-Vision-Center" Fellowship positions at Universidade de Coimbra
-
Santos do Carmo Madeira IV - Work Plan / Goals to be achieved: Federated learning (FL) has emerged as a promising method to address reliability and safety problem by enabling decentralized model training
-
; • Organization, systematization, and management of laboratory data. The candidate will also participate in the integration of experimental results with bioinformatics analyses and machine learning methodologies
-
' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
-
assessed according to the following weights and criteria: - Criterion 1: Absolute merit of curriculum vitae - Criterion 2: Academic performance in the areas of Machine Learning, Data and Information Fusion
-
: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 40%; VII.II- I – In the evaluation of the interview, candidates' performance
-
%; - Criterion 2: Scientific dissemination actions – 40%; - Criterion 3: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 20%; VII.II
-
-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision
-
-of-the-art models for computer vision based on Machine Learning. - Analysis and Study of existing resources; - Analysis of the state of the art in adversarial attacks and adversarial training and their
-
, and contribute to the growing field of Evolutionary Machine Learning powered by foundation models. V - Initial grant duration: 5 months, as long as it doesn't go beyond the end date of the project V.I
-
of an artificial intelligence (AI) solution for the diagnosis of invasive fungal infections, using microscopy images obtained in laboratory settings with limited resources. Leveraging deep learning models such as