Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
-
Field
-
1 Oct 2025 Job Information Organisation/Company FEUP Department Human Resources Division Research Field Engineering » Computer engineering Computer science » Other Researcher Profile First Stage
-
12 Sep 2025 Job Information Organisation/Company FEUP Department Human Resources Division Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions
-
5 Sep 2025 Job Information Organisation/Company FEUP Department Human Resources Division Research Field Engineering » Electrical engineering Engineering » Computer engineering Researcher Profile
-
benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
-
for photocatalytic degradation and H2 production.Designing multi-objective optimization algorithms to maximize environmental and economic performance. Your RoleAs a PhD researcher, you will: Build and validate hybrid
-
initiatives, particularly in the field of Energy Systems - Energy Transition. The objectives are:; - Development and application of artificial intelligence algorithms for different use cases in the energy
-
://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Development and testing of algorithms and methodologies based
-
://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Development and testing of algorithms and methodologies based
-
. Implementation of signal detection algorithms and triangulation ; 4. Planning and participating in field tests to evaluate system performance; 5. Reporting and disseminating the work developed (ideally with a
-
benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions