Sort by
Refine Your Search
-
. We are now looking for: Three (3) Doctoral Researchers (PhD students) in Machine-Learning-Driven Atomistic Simulations The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro
-
aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
-
supervision of Prof. Vanni Noferini. Your role and goals You will work on the theme of “Matrix nearness problems via Riemannian optimisation’’. Areas of mathematics that are relevant include matrix theory
-
, including: Robot Learning: Creating algorithms that empower robots to learn autonomously from interactions and adjust to new tasks. Manipulation: Enhancing techniques for precise and adaptable object handling
-
aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
-
may invite suitable candidates to interview already during the application period. The positions will be filled as soon as suitable candidates are identified. For additional information, contact Prof
-
to interview already during the application period. The positions will be filled as soon as suitable candidates are identified. For additional information, contact Prof. Anton Zasedatelev, anton.zasedatelev
-
you have an ideal opportunity to utilize your existing skills and learn new physics on these components and put them together for millikelvin feedback. This new research direction initiated by Prof
-
Materials (CQM) group led by Prof. Jose Lado and work under the supervision of RCF Fellow Dr. Adolfo O. Fumega. Requirements We seek a highly motivated student with: A Master’s degree in physics, chemistry or
-
research direction initiated by Prof. Mikko Möttönen is currently funded by Jane and Aatos Erkko Foundation. Your role and goals You are expected to carry out research on the scientific steps required