Sort by
Refine Your Search
-
13 Mar 2025 Job Information Organisation/Company INESC TEC Research Field Engineering » Computer engineering Engineering » Electrical engineering Mathematics » Algorithms Researcher Profile First
-
benefit from health insurance, 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
-
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 WORK PROGRAMME AND TRAINING: The work programme focuses on developing advanced real-time control and protection algorithms to address the evolving challenges in power systems due to the massive
-
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
-
, 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
-
insurance, supported by INESC TEC. 2. OBJECTIVES: Main objectives:; - Implement a demonstrator system using the existing infrastructure at ILab to validate palletising algorithms, integrating robot, conveyor
-
. 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