16 phd-in-architecture-landscape-built-environment Postdoctoral positions at AALTO UNIVERSITY in Finland
Sort by
Refine Your Search
-
Structures (FiRST). The starting date is March 1, 2026, or as mutually agreed. The positions are for a period of two years, with a possible one-year extension. Your experience We expect you to have a PhD
-
to take part in the supervision of PhD students and the teaching activities of the research group. Your network and team Currently, our research group consists of 1 professor, 1 lecturer, 1 staff scientist
-
and have a PhD in a field related to mathematical modeling and experience in optimizing industrial processes, this might be something for you! We are looking for a postdoctoral researcher (“PostDoc”) to
-
a dynamic, international research environment. Your experience and ambitions Required Qualifications PhD in Electrical Engineering, Physics, Photonics, Materials Science, or related field Strong
-
, and supportive team environment. Your responsibilities will include: Your primary task will be to carry out research on the electrocatalytic upcycling of plastic waste. This involves: (a) Developing
-
in topological materials and eagerness to work on tough experimental challenges. We expect the candidates to have: PhD degree in physics or in a nearby field Experience on 2D materials and quantum
-
superconducting materials and eagerness to work on tough experimental challenges at low temperatures. We expect the candidates to have: PhD degree in physics or in a nearby field Experience on silicon materials, 2D
-
testing and analyzing experimental results Participating in commercialization of developed materials Qualifications: PhD in one of the following broader experimental areas: physics, chemistry, materials
-
Researcher to join the group. The research project is concerned with the challenging problem of modeling the complex modern radio environment, where a diverse set of devices and agents share the available
-
collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training data to test environments, which is necessary to resolve distribution shifts, hidden confounders