Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Universidade de Coimbra
- INESC ID
- Universidade do Minho - ISISE
- University of Porto
- ADDF - Associacao para o Desenvolvimento do Departamento de Fisica
- Centro de Engenharia Biológica da Universidade do Minho
- FEUP
- Instituto Superior de Agronomia
- NOVA Information Management School (NOVA IMS)
- UNINOVA - Instituto de Desenvolvimento de Novas Tecnologias
- Universidade do Algarve
- Universidade do Minho
- 2 more »
- « less
-
Field
-
) develop a methodology based on image processing algorithms to correct movements not caused by mechanical excitation of the retina; iii) evaluate the feasibility of passive retinal OCE with excitation based
-
quality of water stored therein; b) calibrate and validate models or algorithms based on spectral signatures, associated with in situ validation campaigns; c) extract indicators of spectral signatures
-
capacity to process complex simulation data, fine-tuning its interpretation algorithms, and ensuring that gap-filling recommendations are both biologically plausible and supported by external resources
-
: - Consolidated knowledge in ECC and LDPC algorithms - Consolidated knowledge in hardware description languages and hardware-prototyping toolchains (e.g., Xilinx Vivado) - Knowledge in FPGA
-
study its impact on the degree of collaboration in hybrid teams. The successful candidate will: Develop algorithms to model team performance based on interpersonal (e.g., monitoring, communication) and
-
: Miguel Sá Sousa Castelo Branco IV - Work Plan / Goals to be achieved: To develop and test algorithms that can provide neurofeedback in real time from neurophysiological data. V - Initial grant duration: 12
-
of three-dimensional inference algorithms from object views, based on Generative Networks for Robotics Applications. V - Initial grant duration: 5 months, as long as it doesn't go beyond the end date
-
Intelligence and Algorithms – 30%; VII.II- I – In the evaluation of the interview, candidates' performance will be assessed according to the following weights and criteria: - Criterion 1: Knowledge and profile
-
that do not confer an academic degree, in the area or area related to that requested in the tender. Preferential factors: Have demonstrable experience in the use of machine learning algorithms applied
-
, simulations, and optimization algorithms. Participation in the preparation of technical reports, scientific publications, and dissemination and exploitation activities of the project results. The postdoctoral