Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
posted here will be among the first ones to start in the centre. Please see the centre’s website: Future Aluminium Structures (FAST) - NTNU PhD Position 1: Modelling Plastic Flow and Fracture in Recycled
-
, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines
-
the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB + thermal wound images
-
already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
-
lightweight deep learning model for welding defect recognition. Weld. World. https://doi.org/10.1007/s40194-024-01759-9 J. Franke, F. Heinrich, R.T. Reisch, “Vision based process monitoring in wire arc additive
-
, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling
-
-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
-
exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point for courses included in
-
Learning-enabled control and reinforcement learning Power system operations, planning, and electricity market design Transportation systems modeling and optimization Responsibilities: Postdoctoral fellows
-
intelligence as applied to trauma systems and acute care surgery. Fellows will engage in cutting-edge research spanning multiple domains, including risk prediction models for surgical complications, clinical