75 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral research jobs in Brazil
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
requirements: Background in water resources management, water, and public health. How to apply: Send to nardocci@usp.br : 1) CV, 2) letter of interest, 3) letter of recommendation. Where to apply Website http
-
Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site
-
: Artificial intelligence applied to seismics, neural networks, machine learning, synthetic data generation, seismic inversion, geological CO2 storage. Abstract: This research project aims to develop a synthetic
-
learning-based segmentation, multimodal image fusion, and radiomic feature extraction to construct clinically relevant prognostic models. Conducted at the Heart Institute (InCor) of Hospital das Clínicas
-
publications abd advanced English proficiency. Experience in student supervision is desirable. Candidates must also meet the eligibility criteria for FAPESP post-doctoral fellowships (see https://fapesp.br/en
-
Science, Computer Engineering, or a related field, completed before the fellowship start date. Candidates should demonstrate experience in at least two of the following areas: compilers, parallel programming
-
and Supply (SAASP). Duration: 36 months (full-time) Expected start: April 2026 (flexible) How to apply Applications should be submitted only through the Google Form: https://docs.google.com/forms/d/e
-
fellowship is at: fapesp.br/oportunidades/9189 . Where to apply Website http://www.fapesp.br/oportunidades/9189 Requirements Additional Information Eligibility criteria Eligible destination country/ies
-
(with email and/or phone). Use the subject line: “PostDoc Remote Sensing”. Applications should be sent to edson.bolfe@embrapa.br by April 30, 2026. Where to apply Website http://www.fapesp.br
-
preparation for use in AI models; - Experience with explainability techniques for Machine Learning models; - Desirable experience with system modernization. To apply, send an email with the subject “Inscrição