Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
- Computer Science
- Engineering
- Biology
- Medical Sciences
- Economics
- Science
- Materials Science
- Business
- Earth Sciences
- Mathematics
- Chemistry
- Linguistics
- Electrical Engineering
- Environment
- Social Sciences
- Arts and Literature
- Education
- Law
- Physics
- Psychology
- Philosophy
- Humanities
- Sports and Recreation
- 13 more »
- « less
-
The advertised doctoral position will be carried out at the University of Pisa (https://www.unipi.it/ ; https://www.biologia.unipi.it/) with the most of the research work carried out at the International Centre
-
Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research group is
-
particle clustering and morphology affect strain localization and damage evolution. Integrate experiments and modelling to create predictive tools for recycled alloy performance. Your immediate leader is
-
machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as
-
Familiarity with cleaning and managing large datasets. Strong writing skills. Preferred Qualifications Statistical skills applying artificial intelligence and data science for societal benefit in predictive
-
educates University employees, HDCS customers and stakeholders about relevant compliance requirements, trends, industry best practices and loss control/injury and illness prevention solutions. Financial
-
policies and procures within and between units and other teams Gathers relevant information and feedback, compare and contrast data, identify relationships and predict and manage failures is critical
-
within the broader context of Industry 4.0 and is part of an ongoing initiative to enable predictive diagnostics for electrical machines. The research The increasing need for predictive maintenance in
-
–2023), the model demonstrated good predictive performance for daily and weekly dengue cases based on two years of sentinel hospital data. As part of the ANRS SEA-ROADS programme (2024–2027), coordinated
-
the ESRC project Generations of London English: Dialect and Social Change in Real Time. Project website: https://generationsoflondonenglish.org/ Led by Principal Investigator Professor Devyani Sharma