Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
students pursue their Ph.D. in a similar area, which plenty of opportunity to collaborate and learn from and with peers. About the research project This ad is for a Ph.D. student researcher that will work in
-
presents a unique opportunity to join a cohort of other doctoral researchers in the research school and learn alongside each other in carefully designed courses that align with the excellence centre’s
-
development. We offer high-quality education at the bachelor's, master's, and doctoral levels, delivering over 120 courses annually. The department maintains extensive collaborations with academia, industry
-
humans and society at large is either fully automated or heavily relies on automatically provided decision support. While machine learning approaches become increasingly prevalent in this context
-
/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
-
quantum technology, in academia or industry You have a collaborative attitude and an interest in working both independently and collaboratively in a team environment, sharing best practices and assuming
-
courses, including several master’s programmes. Learn more at: www.chalmers.se/en/departments/e2 Qualifications To qualify, you must: Hold a Master’s degree (or equivalent, 240 ECTS) in Engineering Physics
-
on forests and forestry as complex socio-ecological systems. We closely collaborate with multiple stakeholders and conduct applied research in silviculture, forest ecology, pathology, policy and planning. We
-
student is supposed to learn how forestry works and to conduct tasks independently. It will also create opportunities to connect research to applications in operational work. Qualifications: We are looking
-
demonstrate a willingness to work independently and strong communication abilities to work in collaboration within a team. Meritorious for the position: Experience with deep learning model development and/or