Sort by
Refine Your Search
-
articles. Co-supervise BSc and MSc student projects. Complete PhD thesis. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year
-
model checking. You should be well versed in basic statistics and practical programming skills is a must. Knowledge about the inner workings of GenAI would be nice but not necessary. You must have a two
-
., linear algebra, statistics, optimization, and calculus) is expected, along with programming experience using deep learning frameworks in Python (e.g., PyTorch). While prior knowledge of machine learning
-
letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale A research statement (2-3 pages) explaining your ideas and relevant literature
-
and BSc/MSc diploma (in English) including official description of grading scale A short PhD project proposal (maximum 2 pages plus references) describing a possible research objective, theoretical
-
teaching courses and co-supervision of BSc and MSc. Qualifications MSc graduates with a background in either engineering, mathematics, computer science, computer engineering, physics, sustainable energy
-
. This entails new models for integrating choice and process data, new statistical inference procedures tailored to such models, and new methods for collecting rich behavioural data in immersive experiments
-
quantitative metrics of faults and defects, integrating statistical metrics into active inspection behaviors. Collaborate with a multidisciplinary team—from the AUTOASSESS project—to integrate your algorithms
-
from many different sources into data frames that can be analyzed with biostatistical applications in the statistical software R/Python. Performing data analysis in accordance with time-structures and
-
is to create and combine knowledge on relevant atmospheric flow statistics with AWE time-domain analysis and uncertainty quantification, to determine loads statistics and failure probabilities