-
, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
-
criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
-
focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
-
for analysing authentic, synthetic, and manipulated images or videos. The work will combine predictive performance with explainability, uncertainty estimation, robustness, and generalization. Attention will be
-
TOEFL or IELTS. To be considered for admission, applicants must have a minimum TOEFL score of 575 (written test)/233 (computer-based test)/90 (internet-based test) or a IELTS score of 6.5. In some cases
-
-Computer Interaction (HCI) perspectives to investigate how AI companionship technologies may reshape experiences of care, companionship, emotional support, wellbeing, and autonomy among older adults. Your
-
learning activities in courses related to communications and networking Required selection criteria You must have a relevant master’s degree in wireless communications and mobile networks, computer networks
-
develop quantitative methods to estimate effects on infrastructure degradation, maintenance needs, operational risk, punctuality and costs. The aim is to develop and validate a practical, transparent
-
other components. The PhD candidate will link TrainGate detections and early warnings with maintenance, incident, operational and cost data and develop quantitative methods to estimate effects
-
with the same information and operational constraints. The project will also explore how AI can be used to approximate, accelerate, contextualize, or enhance optimization, for example by learning future