-
of Higher Education Institutions. Preference factors: Previous development of signal processing algorithms in the health area. Willingness to continue the work on a master's thesis work. Minimum requirements
-
for complementary urine biomarker based analysis.; The objectives of this scholarship are: Design and implement a computer vision algorithm for microscopic image analysis for urine biomarker characterization Validate
-
of the robot arm.; • Develop the software for the intelligent robotic programming module: implement algorithms (e.g. based on machine learning) for adaptive rehabilitation exercises; • Test and evaluate
-
holders" (https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms
-
of technical reports on communications protocols, algorithms, and mechanisms developed; - develop new modules enabling the simulation and/or experimentation of emerging wireless networks; - write publications in
-
of the Fellowship are: 1) Apply reinforcement learning algorithms to optimize electrical networks considering energy transition challenges. 2) Develop and apply a protocol for assessing the robustness and safety
-
, supported by INESC TEC. 2. OBJECTIVES: The objectives for this grant are as follows:; • Research and develop machine learning algorithms for the processing of gastric endoscopy images.; • Lead or support the
-
TEC. 2. OBJECTIVES: Specification and development of FLIR algorithm for transmission and distribution networks with integrating high shares of distributed energy resources - Application integration and
-
. OBJECTIVES: Literature review on explainability, fairness, trustability, and accountability in network data; Identify and select the appropriate methods, datasets, and algorithms for the studies about
-
. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Literature review on anomaly detection in network data; Using deep