Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
-
of Machine Learning in Finance (3) Empirical Finance and Climate Finance We provide a rich and engaging research environment for the student. The PhD scholarship offers an attractive package including a tax
-
learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
-
-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
-
processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
-
PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
-
Supervisors: Prof Ioan Notingher (School of Physics and Astronomy) Dr George Gordon and Dr Abdelkhalick Mohammad (Faculty of Engineering) Funding: fully-funded (stipend and PhD fees) Start date
-
certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
-
fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
-
Job Description The section for Atomic Scale Materials Modelling (ASM) at DTU Energy is looking for two outstanding candidates for PhD scholarships within the field of Geometric deep learning