Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
-
ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
-
construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in a structured manner and must be willing and able to cooperate
-
. The successful PhD candidate will collaborate closely as part of an interdisciplinary team consisting of formulation scientists, microbiologists and computer scientists. As a PhD candidate at OsloMet, you will
-
experience, submit them. Transcripts and diplomas for Bachelor's and Master's degrees. If you have not yet completed your Master's thesis, you must provide confirmation on your estimated date
-
Fulfil administrative and teaching duties required (if applicable) Be prepared for changes to your work duties after employment. Required selection criteria You must have a relevant background in Computer
-
interdisciplinary center with joint efforts in theory, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by
-
references Additional relevant documentation of professional knowledge (for example, list of scientific works). If it is difficult to judge the applicant’s contribution for publications with multiple authors
-
the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
-
to a five-year Norwegian degree program, where 120 credits are obtained at master's level Preferred qualifications: Experience with computer-based analytical tools Experience with experimental evolution