Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oxford
- University of Oxford;
- King's College London
- AALTO UNIVERSITY
- Durham University
- KINGS COLLEGE LONDON
- UNIVERSITY OF VIENNA
- University of Cambridge;
- ;
- University of Liverpool
- University of Liverpool;
- Aston University
- Bournemouth University;
- Cardiff University
- Imperial College London
- Lancaster University
- Nature Careers
- Queen Mary University of London;
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Bath
- University of Birmingham
- University of Glasgow;
- University of Leicester
- 13 more »
- « less
-
Field
-
experiments for investigating the neural mechanisms underlying habitual behaviours and learning adaptation to uncertainty. You will use fMRI and neurostimulatory techniques (ultrasound neurostimulation and/or
-
time, we promote a supportive culture that fosters knowledge exchange, learning, creativity and scientific excellence. The PI, the group, the Department and university-wide initiatives (e.g., the Durham
-
-in-cell computer codes hosted on local and national high-performance computing clusters; establishing all-optical diagnostics to map temperature evolution in plasma accelerators; exploring novel inter
-
proof-of-principle repetition-rate and staging experimentation. The successful candidate will perform duties that include developing/using particle-in-cell computer codes hosted on local and national high
-
thriving community of researchers working in related subject areas. You will design and conduct research into human behaviour (habits, learning under uncertainty) that is of an international standard, and
-
, learning under uncertainty) that is of an international standard, and that is carried out expertly, rigorously and in accordance with ethical guidelines. You will also participate actively in the lab
-
have: Experience with AI and machine learning for medical images Experience working with data from multiple hospitals An interest in rare disease research. Additional information Informal enquiries
-
) Limited until: 30.04.2032 Reference no.: 5022 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate
-
to model and simulate building energy systems. Use of machine learning techniques to forecast cooling demand. Proven ability to analyse complex information, including large datasets, and summarise
-
for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in