Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
Your profile PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science. Candidates should have an exceptional academic record and a robust mathematical
-
Prof. A. Lucchi at the Department of Mathematics and Computer Science at the University of Basel are inviting applications for a PhD position focused on the foundations and applications of reasoning in
-
machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
-
, biology, or a closely related discipline Desirable experience: optics and photonics, AI/machine learning, biology, or biomedical sciences Excellent English, analytical, and problem-solving skills UK
-
environment with excellent infrastructure. The candidate will benefit from joint supervision by Prof. Olga Fink (EPFL IMOS) and the UESL team at Empa, combining cutting-edge expertise in machine learning and
-
Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
-
renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
-
elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted
-
-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and machine learning approaches. These are similar to earlier work
-
Compression of quantum data under unreliable entanglement assistance Joint compression and error correction for robust communication in the quantum-classical internet Quantum embeddings for machine learning