Sort by
Refine Your Search
-
, friendly and inspiring, and the position represents a unique opportunity for career development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling
-
for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential data settings, and who
-
both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and
-
-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
-
Equinor. The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine Learning, Department of Informatics. For more information about the position, see https
-
-fellow-in-deep-learning-for-imaging Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/287578/phd-research-fellow-in-de… Requirements Research FieldComputer scienceEducation LevelMaster
-
the final exam. Desired qualifications: Experience in areas such as machine learning, computer vision, control sys-tems, perception, control engineering, or autonomous systems Familiarity with ROS (Robot
-
development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after commencement of the fellowship period. The department is responsible for ensuring
-
the methods to innovations in close collaboration with Aker BP and/or Equinor. The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine Learning, Department
-
measurement quality issues related to respondent non-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models