Sort by
Refine Your Search
-
Listed
-
Employer
- KTH Royal Institute of Technology
- SciLifeLab
- Umeå University
- Chalmers tekniska högskola
- Kungliga Tekniska högskolan
- Chalmers University of Technology
- Karolinska Institutet (KI)
- Lunds universitet
- University of Lund
- Örebro University
- Epishine
- Karlstad University
- Nature Careers
- Sveriges lantbruksuniversitet
- Swedish University of Agricultural Sciences
- 5 more »
- « less
-
Field
-
candidates recruited for this international project will receive training at the forefront of research and innovation in organic electronics. Moreover, the network will provide Doctoral candidates with
-
inherent complexities. Your role: The doctoral student will conduct research at the intersection of optimization, game theory, and automatic control for complex systems. Their work will encompass both
-
studies within the Marie Skłodowska-Curie Actions (MSCA ) funded network program for PhD students, IDEAL4GREEN , that addresses the urgent challenges of climate change and the global shift towards
-
for large-scale simulations of cortical memory function. Special focus is on coupling multiple neural networks to study neural interactions between different cortical regions supporting cognitive function
-
visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and
-
control strategies that strengthen their disturbance-handling capability. The work will be carried out with in a MSCA-DN doctoral training network with research stays at DNV in the Netherlands, Aalborg
-
northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
-
. The research team focuses on developing novel methods to extract knowledge from data, modeling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but
-
advanced methods in AI and machine learning, combined with atomistic spin dynamics and first-principles electronic structure calculations, to study complex nanomagnetism. A specific goal of the project is to
-
(CFD) of new solutions for capturing biogenic CO2 from point sources. The work will consist in creating simulations tools based on CFD to simulate details on CO2 capture by solvents in complex geometries